Overview

Dataset statistics

Number of variables13
Number of observations44
Missing cells108
Missing cells (%)18.9%
Duplicate rows2
Duplicate rows (%)4.5%
Total size in memory4.8 KiB
Average record size in memory112.0 B

Variable types

DateTime1
Text4
Categorical8

Dataset

Description한국환경산업기술원 환경정책자금융자시스템 천연가스 저장용기 데이터(등록자 비식별화, 용기 수, 단위용기 등) 2020년도 정보 입니다.
URLhttps://www.data.go.kr/data/15120431/fileData.do

Alerts

용기 수 비고 has constant value ""Constant
Dataset has 2 (4.5%) duplicate rowsDuplicates
사용압력 중압 is highly overall correlated with 용기 수 고압 and 5 other fieldsHigh correlation
사용압력 저압 is highly overall correlated with 용기 수 고압 and 5 other fieldsHigh correlation
용기 수 저압 is highly overall correlated with 용기 수 고압 and 5 other fieldsHigh correlation
용기 수 중압 is highly overall correlated with 용기 수 고압 and 5 other fieldsHigh correlation
사용압력 고압 is highly overall correlated with 용기 수 고압 and 6 other fieldsHigh correlation
용기 수 고압 is highly overall correlated with 용기 수 중압 and 5 other fieldsHigh correlation
단위용기 체적 중압 is highly overall correlated with 용기 수 고압 and 5 other fieldsHigh correlation
적용규격 고압 is highly overall correlated with 사용압력 고압High correlation
용기 수 중압 is highly imbalanced (73.3%)Imbalance
용기 수 저압 is highly imbalanced (73.4%)Imbalance
단위용기 체적 중압 is highly imbalanced (80.3%)Imbalance
사용압력 중압 is highly imbalanced (80.3%)Imbalance
사용압력 저압 is highly imbalanced (76.6%)Imbalance
용기 수 비고 has 43 (97.7%) missing valuesMissing
단위용기 체적 고압 has 24 (54.5%) missing valuesMissing
단위용기 체적 저압 has 41 (93.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:28:26.568514
Analysis finished2023-12-12 22:28:27.738771
Duration1.17 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록일자
Date

Distinct38
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size484.0 B
Minimum2015-01-13 00:00:00
Maximum2020-11-30 00:00:00
2023-12-13T07:28:27.790821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:27.897870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)

등록자
Text

Distinct24
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-13T07:28:28.059392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters440
Distinct characters15
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

Unique16 ?
Unique (%)36.4%

Sample

1st row0000022421
2nd row0000020033
3rd rowA210111534
4th row0000020036
5th row0000010712
ValueCountFrequency (%)
0000020036 8
18.2%
0000011090 5
 
11.4%
mgr0003914 4
 
9.1%
0000010712 3
 
6.8%
a210111534 2
 
4.5%
c201090001 2
 
4.5%
mgr0005400 2
 
4.5%
0000008453 2
 
4.5%
0000016749 1
 
2.3%
0000022421 1
 
2.3%
Other values (14) 14
31.8%
2023-12-13T07:28:28.332359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 229
52.0%
1 53
 
12.0%
2 27
 
6.1%
3 26
 
5.9%
6 19
 
4.3%
9 17
 
3.9%
4 14
 
3.2%
5 10
 
2.3%
M 8
 
1.8%
G 8
 
1.8%
Other values (5) 29
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 409
93.0%
Uppercase Letter 31
 
7.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 229
56.0%
1 53
 
13.0%
2 27
 
6.6%
3 26
 
6.4%
6 19
 
4.6%
9 17
 
4.2%
4 14
 
3.4%
5 10
 
2.4%
8 8
 
2.0%
7 6
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
M 8
25.8%
G 8
25.8%
R 8
25.8%
A 5
16.1%
C 2
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 409
93.0%
Latin 31
 
7.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 229
56.0%
1 53
 
13.0%
2 27
 
6.6%
3 26
 
6.4%
6 19
 
4.6%
9 17
 
4.2%
4 14
 
3.4%
5 10
 
2.4%
8 8
 
2.0%
7 6
 
1.5%
Latin
ValueCountFrequency (%)
M 8
25.8%
G 8
25.8%
R 8
25.8%
A 5
16.1%
C 2
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 229
52.0%
1 53
 
12.0%
2 27
 
6.1%
3 26
 
5.9%
6 19
 
4.3%
9 17
 
3.9%
4 14
 
3.2%
5 10
 
2.3%
M 8
 
1.8%
G 8
 
1.8%
Other values (5) 29
 
6.6%

용기 수 고압
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
24 
1
8 
3
4 
2
3 
6
 
2
Other values (3)
3 

Length

Max length4
Median length4
Mean length2.6590909
Min length1

Unique

Unique3 ?
Unique (%)6.8%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row2

Common Values

ValueCountFrequency (%)
<NA> 24
54.5%
1 8
 
18.2%
3 4
 
9.1%
2 3
 
6.8%
6 2
 
4.5%
7 1
 
2.3%
7개 1
 
2.3%
8 1
 
2.3%

Length

2023-12-13T07:28:28.460565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:28.565661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 24
54.5%
1 8
 
18.2%
3 4
 
9.1%
2 3
 
6.8%
6 2
 
4.5%
7 1
 
2.3%
7개 1
 
2.3%
8 1
 
2.3%

용기 수 중압
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
42 
1
 
2

Length

Max length4
Median length4
Mean length3.8636364
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 42
95.5%
1 2
 
4.5%

Length

2023-12-13T07:28:28.744823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:28.859227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 42
95.5%
1 2
 
4.5%

용기 수 저압
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
41 
1
 
2
2
 
1

Length

Max length4
Median length4
Mean length3.7954545
Min length1

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 41
93.2%
1 2
 
4.5%
2 1
 
2.3%

Length

2023-12-13T07:28:28.976205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:29.088961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 41
93.2%
1 2
 
4.5%
2 1
 
2.3%

용기 수 비고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing43
Missing (%)97.7%
Memory size484.0 B
2023-12-13T07:28:29.197786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters9
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

Unique1 ?
Unique (%)100.0%

Sample

1st rowCASCADE TYPE
ValueCountFrequency (%)
cascade 1
50.0%
type 1
50.0%
2023-12-13T07:28:29.454035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 2
16.7%
A 2
16.7%
E 2
16.7%
S 1
8.3%
D 1
8.3%
1
8.3%
T 1
8.3%
Y 1
8.3%
P 1
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11
91.7%
Space Separator 1
 
8.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 2
18.2%
A 2
18.2%
E 2
18.2%
S 1
9.1%
D 1
9.1%
T 1
9.1%
Y 1
9.1%
P 1
9.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11
91.7%
Common 1
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 2
18.2%
A 2
18.2%
E 2
18.2%
S 1
9.1%
D 1
9.1%
T 1
9.1%
Y 1
9.1%
P 1
9.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 2
16.7%
A 2
16.7%
E 2
16.7%
S 1
8.3%
D 1
8.3%
1
8.3%
T 1
8.3%
Y 1
8.3%
P 1
8.3%

단위용기 체적 고압
Text

MISSING 

Distinct11
Distinct (%)55.0%
Missing24
Missing (%)54.5%
Memory size484.0 B
2023-12-13T07:28:29.615474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10.5
Mean length6.55
Min length3

Characters and Unicode

Total characters131
Distinct characters19
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

Unique8 ?
Unique (%)40.0%

Sample

1st row130
2nd row50GPM
3rd row1300L
4th row60000Liter
5th row1,300
ValueCountFrequency (%)
60000liter 5
22.7%
1300 5
22.7%
1300l 2
 
9.1%
130 1
 
4.5%
50gpm 1
 
4.5%
1,300 1
 
4.5%
44000l 1
 
4.5%
44,000l 1
 
4.5%
1
 
4.5%
2기 1
 
4.5%
Other values (3) 3
13.6%
2023-12-13T07:28:29.936679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 52
39.7%
L 11
 
8.4%
3 11
 
8.4%
1 10
 
7.6%
6 6
 
4.6%
i 6
 
4.6%
t 6
 
4.6%
e 6
 
4.6%
r 6
 
4.6%
4 4
 
3.1%
Other values (9) 13
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 85
64.9%
Lowercase Letter 24
 
18.3%
Uppercase Letter 14
 
10.7%
Other Punctuation 5
 
3.8%
Space Separator 2
 
1.5%
Other Letter 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52
61.2%
3 11
 
12.9%
1 10
 
11.8%
6 6
 
7.1%
4 4
 
4.7%
2 1
 
1.2%
5 1
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
L 11
78.6%
M 1
 
7.1%
P 1
 
7.1%
G 1
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
i 6
25.0%
t 6
25.0%
e 6
25.0%
r 6
25.0%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
* 1
 
20.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Letter
ValueCountFrequency (%)
기 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 92
70.2%
Latin 38
29.0%
Hangul 1
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52
56.5%
3 11
 
12.0%
1 10
 
10.9%
6 6
 
6.5%
4 4
 
4.3%
, 4
 
4.3%
2
 
2.2%
2 1
 
1.1%
* 1
 
1.1%
5 1
 
1.1%
Latin
ValueCountFrequency (%)
L 11
28.9%
i 6
15.8%
t 6
15.8%
e 6
15.8%
r 6
15.8%
M 1
 
2.6%
P 1
 
2.6%
G 1
 
2.6%
Hangul
ValueCountFrequency (%)
기 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
99.2%
Hangul 1
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52
40.0%
L 11
 
8.5%
3 11
 
8.5%
1 10
 
7.7%
6 6
 
4.6%
i 6
 
4.6%
t 6
 
4.6%
e 6
 
4.6%
r 6
 
4.6%
4 4
 
3.1%
Other values (8) 12
 
9.2%
Hangul
ValueCountFrequency (%)
기 1
100.0%

단위용기 체적 중압
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
42 
130
 
1
1300
 
1

Length

Max length4
Median length4
Mean length3.9772727
Min length3

Unique

Unique2 ?
Unique (%)4.5%

Sample

1st row130
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 42
95.5%
130 1
 
2.3%
1300 1
 
2.3%

Length

2023-12-13T07:28:30.077051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:30.183414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 42
95.5%
130 1
 
2.3%
1300 1
 
2.3%

단위용기 체적 저압
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing41
Missing (%)93.2%
Memory size484.0 B
2023-12-13T07:28:30.295073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6666667
Min length3

Characters and Unicode

Total characters11
Distinct characters5
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

Unique3 ?
Unique (%)100.0%

Sample

1st row130
2nd row50M3
3rd row1300
ValueCountFrequency (%)
130 1
33.3%
50m3 1
33.3%
1300 1
33.3%
2023-12-13T07:28:30.551890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4
36.4%
3 3
27.3%
1 2
18.2%
5 1
 
9.1%
M 1
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
90.9%
Uppercase Letter 1
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
40.0%
3 3
30.0%
1 2
20.0%
5 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10
90.9%
Latin 1
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4
40.0%
3 3
30.0%
1 2
20.0%
5 1
 
10.0%
Latin
ValueCountFrequency (%)
M 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4
36.4%
3 3
27.3%
1 2
18.2%
5 1
 
9.1%
M 1
 
9.1%

사용압력 고압
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
24 
12kg/cm2
6 
250
5 
253
4 
12
3 
Other values (2)
 
2

Length

Max length17
Median length4
Mean length4.5227273
Min length2

Unique

Unique2 ?
Unique (%)4.5%

Sample

1st row250
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row12

Common Values

ValueCountFrequency (%)
<NA> 24
54.5%
12kg/cm2 6
 
13.6%
250 5
 
11.4%
253 4
 
9.1%
12 3
 
6.8%
27.6Mpa(4,000PSI) 1
 
2.3%
255.1 1
 
2.3%

Length

2023-12-13T07:28:30.698792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:30.829613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 24
54.5%
12kg/cm2 6
 
13.6%
250 5
 
11.4%
253 4
 
9.1%
12 3
 
6.8%
27.6mpa(4,000psi 1
 
2.3%
255.1 1
 
2.3%

사용압력 중압
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
42 
250.0
 
1
255.1
 
1

Length

Max length5
Median length4
Mean length4.0454545
Min length4

Unique

Unique2 ?
Unique (%)4.5%

Sample

1st row250.0
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 42
95.5%
250.0 1
 
2.3%
255.1 1
 
2.3%

Length

2023-12-13T07:28:30.959632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:31.080107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 42
95.5%
250.0 1
 
2.3%
255.1 1
 
2.3%

사용압력 저압
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
41 
250.0
 
1
12.0
 
1
255.1
 
1

Length

Max length5
Median length4
Mean length4.0454545
Min length4

Unique

Unique3 ?
Unique (%)6.8%

Sample

1st row250.0
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 41
93.2%
250.0 1
 
2.3%
12.0 1
 
2.3%
255.1 1
 
2.3%

Length

2023-12-13T07:28:31.204304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:31.309269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 41
93.2%
250.0 1
 
2.3%
12.0 1
 
2.3%
255.1 1
 
2.3%

적용규격 고압
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
25 
ASME
5 
KGSAC111
4 
KGS
3 
ASEM
 
2
Other values (4)
5 

Length

Max length46
Median length4
Mean length6.3181818
Min length3

Unique

Unique3 ?
Unique (%)6.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 25
56.8%
ASME 5
 
11.4%
KGSAC111 4
 
9.1%
KGS 3
 
6.8%
ASEM 2
 
4.5%
KGS AC111 2
 
4.5%
609.6 X 6,250 1
 
2.3%
ASME code section vlll,division 1 , appendx 22 1
 
2.3%
ASME, Sec, Vlll, Div, 1Appendx22 1
 
2.3%

Length

2023-12-13T07:28:31.406717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:31.516583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 25
42.4%
asme 7
 
11.9%
kgs 5
 
8.5%
kgsac111 4
 
6.8%
asem 2
 
3.4%
ac111 2
 
3.4%
1
 
1.7%
div 1
 
1.7%
vlll 1
 
1.7%
sec 1
 
1.7%
Other values (10) 10
 
16.9%

Correlations

2023-12-13T07:28:31.633889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록일자등록자용기 수 고압용기 수 저압단위용기 체적 고압단위용기 체적 중압단위용기 체적 저압사용압력 고압사용압력 중압사용압력 저압적용규격 고압
등록일자1.0000.9731.0001.0000.9060.0001.0001.0000.0001.0001.000
등록자0.9731.0000.9021.0000.9020.0001.0000.9880.0001.0000.901
용기 수 고압1.0000.9021.000NaN0.557NaNNaN0.737NaNNaN0.686
용기 수 저압1.0001.000NaN1.000NaNNaN1.000NaNNaN1.000NaN
단위용기 체적 고압0.9060.9020.557NaN1.0000.0000.0000.8790.0000.0000.915
단위용기 체적 중압0.0000.000NaNNaN0.0001.0000.0000.0000.0000.000NaN
단위용기 체적 저압1.0001.000NaN1.0000.0000.0001.0000.0000.0001.000NaN
사용압력 고압1.0000.9880.737NaN0.8790.0000.0001.0000.0000.0000.977
사용압력 중압0.0000.000NaNNaN0.0000.0000.0000.0001.0000.000NaN
사용압력 저압1.0001.000NaN1.0000.0000.0001.0000.0000.0001.000NaN
적용규격 고압1.0000.9010.686NaN0.915NaNNaN0.977NaNNaN1.000
2023-12-13T07:28:31.802388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적용규격 고압사용압력 중압사용압력 저압용기 수 저압용기 수 중압사용압력 고압용기 수 고압단위용기 체적 중압
적용규격 고압1.000NaNNaNNaNNaN0.8580.408NaN
사용압력 중압NaN1.0001.0001.0001.0001.0001.0001.000
사용압력 저압NaN1.0001.0001.0001.0001.0001.0001.000
용기 수 저압NaN1.0001.0001.0001.0001.0001.0001.000
용기 수 중압NaN1.0001.0001.0001.0001.0001.0001.000
사용압력 고압0.8581.0001.0001.0001.0001.0000.5201.000
용기 수 고압0.4081.0001.0001.0001.0000.5201.0001.000
단위용기 체적 중압NaN1.0001.0001.0001.0001.0001.0001.000
2023-12-13T07:28:31.937115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용기 수 고압용기 수 중압용기 수 저압단위용기 체적 중압사용압력 고압사용압력 중압사용압력 저압적용규격 고압
용기 수 고압1.0001.0001.0001.0000.5201.0001.0000.408
용기 수 중압1.0001.0001.0001.0001.0001.0001.000NaN
용기 수 저압1.0001.0001.0001.0001.0001.0001.000NaN
단위용기 체적 중압1.0001.0001.0001.0001.0001.0001.000NaN
사용압력 고압0.5201.0001.0001.0001.0001.0001.0000.858
사용압력 중압1.0001.0001.0001.0001.0001.0001.000NaN
사용압력 저압1.0001.0001.0001.0001.0001.0001.000NaN
적용규격 고압0.408NaNNaNNaN0.858NaNNaN1.000

Missing values

2023-12-13T07:28:27.278571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:28:27.459212image/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.
2023-12-13T07:28:27.623727image/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

등록일자등록자용기 수 고압용기 수 중압용기 수 저압용기 수 비고단위용기 체적 고압단위용기 체적 중압단위용기 체적 저압사용압력 고압사용압력 중압사용압력 저압적용규격 고압
02020-11-300000022421111<NA>130130130250250.0250.0<NA>
12020-05-200000020033<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
22020-03-19A210111534<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32020-03-180000020036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
42020-03-2000000107122<NA><NA><NA>50GPM<NA><NA>12<NA><NA>KGS
52020-03-110000020036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
62020-03-110000020036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
72020-02-050000020036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
82020-01-200000020036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
92020-01-200000020036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
등록일자등록자용기 수 고압용기 수 중압용기 수 저압용기 수 비고단위용기 체적 고압단위용기 체적 중압단위용기 체적 저압사용압력 고압사용압력 중압사용압력 저압적용규격 고압
342015-12-21MGR0003914<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
352015-12-02A2101132583<NA><NA><NA>1,300L<NA><NA>27.6Mpa(4,000PSI)<NA><NA>ASME code section vlll,division 1 , appendx 22
362015-11-06MGR0005400111<NA>130013001300255.1255.1255.1ASME, Sec, Vlll, Div, 1Appendx22
372015-06-2600000083568<NA><NA><NA>1300<NA><NA>253<NA><NA>ASME
382015-05-27C2010900013<NA><NA><NA>1300L<NA><NA>250<NA><NA>ASEM
392015-04-13A210112289<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
402015-01-260000007643<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
412015-01-26A210111534<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
422015-01-20MGR0003914<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
432015-01-13MGR0003914<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

등록일자등록자용기 수 고압용기 수 중압용기 수 저압용기 수 비고단위용기 체적 고압단위용기 체적 중압단위용기 체적 저압사용압력 고압사용압력 중압사용압력 저압적용규격 고압# duplicates
02020-01-200000020036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
12020-03-110000020036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2