Overview

Dataset statistics

Number of variables15
Number of observations752
Missing cells9
Missing cells (%)0.1%
Duplicate rows90
Duplicate rows (%)12.0%
Total size in memory89.0 KiB
Average record size in memory121.2 B

Variable types

Categorical8
Text3
Numeric1
DateTime3

Dataset

Description한국가스공사는 천연가스 거래를 위한 계량설비를 운영 중에 있음. 계량설비에 의해 측정되는 천연가스 공급량은 요금 산출에 직접적인 영향을 미치는 요인으로, 우리 공사는 투명하고 공정한 상거래를 위해 법적기준보다 강화된 사내기준에 의해 정기적인 교정을 실시하고 있음. 우리 공사는 한국인정기구(KOLAS)로부터 인정받은 유량측정센터를 운영하여 계량설비를 교정하고 있으며, 연간 100여대의 유량계와 2700여기의 계측기를 교정하고 있음. 유량측정센터는 국내 유일의 폐루프식 교정시설을 운영하여 다양한 유량 및 압력조건을 연중 내내 발생시킬 수 있어, 높은 교정 정확도와 효율성을 갖고 있음. 발전시 및 도시가스사에게 우리 공사 계량설비 교정 현황 공개를 통해 천연가스의 공정한 상거래 현황을 공유하고자 함.
URLhttps://www.data.go.kr/data/15102658/fileData.do

Alerts

Dataset has 90 (12.0%) duplicate rowsDuplicates
(PDT)(HIGH)제작사 is highly overall correlated with 지역본부 and 4 other fieldsHigh correlation
(PT)제작사 is highly overall correlated with 지역본부 and 5 other fieldsHigh correlation
(PDT)(LOW)제작사 is highly overall correlated with 지역본부 and 4 other fieldsHigh correlation
지역본부 is highly overall correlated with (TT)제작사 and 4 other fieldsHigh correlation
(TT)제작사 is highly overall correlated with 지역본부 and 5 other fieldsHigh correlation
(RTD)제작사 is highly overall correlated with (TT)제작사 and 3 other fieldsHigh correlation
(PDT)최근교정년월 is highly overall correlated with 지역본부 and 2 other fieldsHigh correlation
(PDT)(LOW)제작사 is highly imbalanced (61.9%)Imbalance
(PDT)(HIGH)제작사 is highly imbalanced (58.6%)Imbalance
(PDT)최근교정년월 is highly imbalanced (68.6%)Imbalance

Reproduction

Analysis started2023-12-12 01:11:39.752106
Analysis finished2023-12-12 01:11:41.669381
Duration1.92 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역본부
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
부산경남
112 
경기
103 
서울
94 
광주전남
87 
대구경북
84 
Other values (5)
272 

Length

Max length5
Median length2
Mean length2.9255319
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산경남 112
14.9%
경기 103
13.7%
서울 94
12.5%
광주전남 87
11.6%
대구경북 84
11.2%
전북 76
10.1%
인천 69
9.2%
강원 68
9.0%
대전충청 47
6.2%
제주LNG 12
 
1.6%

Length

2023-12-12T10:11:41.738071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:11:41.869128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산경남 112
14.9%
경기 103
13.7%
서울 94
12.5%
광주전남 87
11.6%
대구경북 84
11.2%
전북 76
10.1%
인천 69
9.2%
강원 68
9.0%
대전충청 47
6.2%
제주lng 12
 
1.6%

관리소
Text

Distinct177
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2023-12-12T10:11:42.246474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0611702
Min length2

Characters and Unicode

Total characters1550
Distinct characters149
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고척
2nd row고척
3rd row고척
4th row고척
5th row고척
ValueCountFrequency (%)
청라 12
 
1.6%
합정 12
 
1.6%
부곡 12
 
1.6%
율촌 12
 
1.6%
양산 11
 
1.5%
분당 10
 
1.3%
울산 9
 
1.2%
석수 9
 
1.2%
온산 9
 
1.2%
중동 9
 
1.2%
Other values (167) 647
86.0%
2023-12-12T10:11:42.751641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 87
 
5.6%
천 56
 
3.6%
동 55
 
3.5%
주 43
 
2.8%
성 37
 
2.4%
원 36
 
2.3%
안 34
 
2.2%
양 34
 
2.2%
정 30
 
1.9%
장 28
 
1.8%
Other values (139) 1110
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1550
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 87
 
5.6%
천 56
 
3.6%
동 55
 
3.5%
주 43
 
2.8%
성 37
 
2.4%
원 36
 
2.3%
안 34
 
2.2%
양 34
 
2.2%
정 30
 
1.9%
장 28
 
1.8%
Other values (139) 1110
71.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1550
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 87
 
5.6%
천 56
 
3.6%
동 55
 
3.5%
주 43
 
2.8%
성 37
 
2.4%
원 36
 
2.3%
안 34
 
2.2%
양 34
 
2.2%
정 30
 
1.9%
장 28
 
1.8%
Other values (139) 1110
71.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1550
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 87
 
5.6%
천 56
 
3.6%
동 55
 
3.5%
주 43
 
2.8%
성 37
 
2.4%
원 36
 
2.3%
안 34
 
2.2%
양 34
 
2.2%
정 30
 
1.9%
장 28
 
1.8%
Other values (139) 1110
71.6%

(유랑계)수요처
Text

Distinct97
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2023-12-12T10:11:43.041613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length6.5930851
Min length2

Characters and Unicode

Total characters4958
Distinct characters158
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.8%

Sample

1st row귀뚜라미에너지
2nd row귀뚜라미에너지
3rd row귀뚜라미에너지
4th row삼천리
5th row삼천리
ValueCountFrequency (%)
삼천리 63
 
7.0%
대성에너지 37
 
4.1%
경남에너지 37
 
4.1%
영남에너지서비스 33
 
3.7%
서울도시가스 28
 
3.1%
해양에너지 27
 
3.0%
전남도시가스 27
 
3.0%
경동도시가스 21
 
2.3%
부산도시가스 21
 
2.3%
충청에너지서비스 20
 
2.2%
Other values (108) 582
65.0%
2023-12-12T10:11:43.475345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 319
 
6.4%
지 250
 
5.0%
도 231
 
4.7%
에 230
 
4.6%
시 222
 
4.5%
가 222
 
4.5%
너 217
 
4.4%
152
 
3.1%
서 141
 
2.8%
전 135
 
2.7%
Other values (148) 2839
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4361
88.0%
Uppercase Letter 301
 
6.1%
Space Separator 152
 
3.1%
Close Punctuation 32
 
0.6%
Open Punctuation 32
 
0.6%
Other Punctuation 30
 
0.6%
Decimal Number 28
 
0.6%
Other Symbol 13
 
0.3%
Math Symbol 6
 
0.1%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 319
 
7.3%
지 250
 
5.7%
도 231
 
5.3%
에 230
 
5.3%
시 222
 
5.1%
가 222
 
5.1%
너 217
 
5.0%
서 141
 
3.2%
전 135
 
3.1%
남 123
 
2.8%
Other values (113) 2271
52.1%
Uppercase Letter
ValueCountFrequency (%)
S 68
22.6%
E 45
15.0%
B 29
9.6%
J 29
9.6%
C 28
9.3%
G 21
 
7.0%
N 17
 
5.6%
I 16
 
5.3%
T 13
 
4.3%
Y 10
 
3.3%
Other values (5) 25
 
8.3%
Decimal Number
ValueCountFrequency (%)
1 9
32.1%
2 6
21.4%
8 3
 
10.7%
7 3
 
10.7%
3 2
 
7.1%
9 1
 
3.6%
4 1
 
3.6%
0 1
 
3.6%
6 1
 
3.6%
5 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
& 12
40.0%
, 10
33.3%
# 8
26.7%
Math Symbol
ValueCountFrequency (%)
~ 3
50.0%
+ 3
50.0%
Space Separator
ValueCountFrequency (%)
152
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4374
88.2%
Latin 301
 
6.1%
Common 283
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 319
 
7.3%
지 250
 
5.7%
도 231
 
5.3%
에 230
 
5.3%
시 222
 
5.1%
가 222
 
5.1%
너 217
 
5.0%
서 141
 
3.2%
전 135
 
3.1%
남 123
 
2.8%
Other values (114) 2284
52.2%
Common
ValueCountFrequency (%)
152
53.7%
) 32
 
11.3%
( 32
 
11.3%
& 12
 
4.2%
, 10
 
3.5%
1 9
 
3.2%
# 8
 
2.8%
2 6
 
2.1%
8 3
 
1.1%
~ 3
 
1.1%
Other values (9) 16
 
5.7%
Latin
ValueCountFrequency (%)
S 68
22.6%
E 45
15.0%
B 29
9.6%
J 29
9.6%
C 28
9.3%
G 21
 
7.0%
N 17
 
5.6%
I 16
 
5.3%
T 13
 
4.3%
Y 10
 
3.3%
Other values (5) 25
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4361
88.0%
ASCII 584
 
11.8%
None 13
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 319
 
7.3%
지 250
 
5.7%
도 231
 
5.3%
에 230
 
5.3%
시 222
 
5.1%
가 222
 
5.1%
너 217
 
5.0%
서 141
 
3.2%
전 135
 
3.1%
남 123
 
2.8%
Other values (113) 2271
52.1%
ASCII
ValueCountFrequency (%)
152
26.0%
S 68
11.6%
E 45
 
7.7%
) 32
 
5.5%
( 32
 
5.5%
B 29
 
5.0%
J 29
 
5.0%
C 28
 
4.8%
G 21
 
3.6%
N 17
 
2.9%
Other values (24) 131
22.4%
None
ValueCountFrequency (%)
㈜ 13
100.0%

(유량계)TYPE
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
터빈
431 
초음파
187 
오리피스
123 
로터리
 
11

Length

Max length4
Median length2
Mean length2.5904255
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
터빈 431
57.3%
초음파 187
24.9%
오리피스 123
 
16.4%
로터리 11
 
1.5%

Length

2023-12-12T10:11:43.614780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:11:43.724699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
터빈 431
57.3%
초음파 187
24.9%
오리피스 123
 
16.4%
로터리 11
 
1.5%

(유량계)SIZE
Real number (ℝ)

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.273936
Minimum2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-12T10:11:43.852475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q16
median10
Q316
95-th percentile16
Maximum24
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.8566082
Coefficient of variation (CV)0.47271154
Kurtosis-0.96316897
Mean10.273936
Median Absolute Deviation (MAD)4
Skewness0.25255962
Sum7726
Variance23.586643
MonotonicityNot monotonic
2023-12-12T10:11:44.001753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
16 171
22.7%
10 140
18.6%
4 137
18.2%
8 82
10.9%
6 81
10.8%
12 78
10.4%
20 32
 
4.3%
14 14
 
1.9%
2 10
 
1.3%
18 3
 
0.4%
Other values (2) 4
 
0.5%
ValueCountFrequency (%)
2 10
 
1.3%
3 2
 
0.3%
4 137
18.2%
6 81
10.8%
8 82
10.9%
10 140
18.6%
12 78
10.4%
14 14
 
1.9%
16 171
22.7%
18 3
 
0.4%
ValueCountFrequency (%)
24 2
 
0.3%
20 32
 
4.3%
18 3
 
0.4%
16 171
22.7%
14 14
 
1.9%
12 78
10.4%
10 140
18.6%
8 82
10.9%
6 81
10.8%
4 137
18.2%

(유량계)최근교정년월
Text

Distinct70
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2023-12-12T10:11:44.281860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.981383
Min length1

Characters and Unicode

Total characters7506
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

Unique11 ?
Unique (%)1.5%

Sample

1st row2019-06-01
2nd row2017-02-01
3rd row2017-02-01
4th row2021-05-01
5th row2019-07-01
ValueCountFrequency (%)
2021-05-01 30
 
4.0%
2022-08-01 29
 
3.9%
2022-05-01 27
 
3.6%
2022-03-01 27
 
3.6%
2021-06-01 26
 
3.5%
2018-06-01 23
 
3.1%
2022-04-01 23
 
3.1%
2021-08-01 23
 
3.1%
2022-06-01 22
 
2.9%
2022-11-01 22
 
2.9%
Other values (60) 500
66.5%
2023-12-12T10:11:44.702411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2235
29.8%
1 1509
20.1%
- 1501
20.0%
2 1403
18.7%
8 191
 
2.5%
9 176
 
2.3%
7 133
 
1.8%
6 111
 
1.5%
5 97
 
1.3%
4 86
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6005
80.0%
Dash Punctuation 1501
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2235
37.2%
1 1509
25.1%
2 1403
23.4%
8 191
 
3.2%
9 176
 
2.9%
7 133
 
2.2%
6 111
 
1.8%
5 97
 
1.6%
4 86
 
1.4%
3 64
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 1501
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7506
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2235
29.8%
1 1509
20.1%
- 1501
20.0%
2 1403
18.7%
8 191
 
2.5%
9 176
 
2.3%
7 133
 
1.8%
6 111
 
1.5%
5 97
 
1.3%
4 86
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2235
29.8%
1 1509
20.1%
- 1501
20.0%
2 1403
18.7%
8 191
 
2.5%
9 176
 
2.3%
7 133
 
1.8%
6 111
 
1.5%
5 97
 
1.3%
4 86
 
1.1%

(TT)제작사
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Rosemount
588 
ROSEMOUNT
106 
YOKOGAWA
 
45
Yokogawa
 
13

Length

Max length9
Median length9
Mean length8.9228723
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Rosemount 588
78.2%
ROSEMOUNT 106
 
14.1%
YOKOGAWA 45
 
6.0%
Yokogawa 13
 
1.7%

Length

2023-12-12T10:11:44.884323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:11:45.058381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rosemount 694
92.3%
yokogawa 58
 
7.7%

(TT)최근교정년월
Date

Distinct13
Distinct (%)1.7%
Missing3
Missing (%)0.4%
Memory size6.0 KiB
Minimum2021-08-01 00:00:00
Maximum2022-12-01 00:00:00
2023-12-12T10:11:45.192759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:45.335500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

(RTD)제작사
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Rosemount
396 
WISE
251 
HISCO
 
38
ROSEMOUNT
 
34
WOOJIN
 
17
Other values (3)
 
16

Length

Max length9
Median length9
Mean length6.9667553
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Rosemount 396
52.7%
WISE 251
33.4%
HISCO 38
 
5.1%
ROSEMOUNT 34
 
4.5%
WOOJIN 17
 
2.3%
Wise 10
 
1.3%
히스코 3
 
0.4%
Yokogawa 3
 
0.4%

Length

2023-12-12T10:11:45.500189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:11:45.668518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rosemount 430
57.2%
wise 261
34.7%
hisco 38
 
5.1%
woojin 17
 
2.3%
히스코 3
 
0.4%
yokogawa 3
 
0.4%

(RTD)최근교정년월
Date

Distinct12
Distinct (%)1.6%
Missing3
Missing (%)0.4%
Memory size6.0 KiB
Minimum2021-07-01 00:00:00
Maximum2022-11-01 00:00:00
2023-12-12T10:11:45.819928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:45.950163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

(PT)제작사
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Rosemount
521 
ROSEMOUNT
172 
YOKOGAWA
 
43
Yokogawa
 
16

Length

Max length9
Median length9
Mean length8.9215426
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Rosemount 521
69.3%
ROSEMOUNT 172
 
22.9%
YOKOGAWA 43
 
5.7%
Yokogawa 16
 
2.1%

Length

2023-12-12T10:11:46.104076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:11:46.226759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rosemount 693
92.2%
yokogawa 59
 
7.8%

(PT)최근교정년월
Date

Distinct13
Distinct (%)1.7%
Missing3
Missing (%)0.4%
Memory size6.0 KiB
Minimum2021-08-01 00:00:00
Maximum2022-12-01 00:00:00
2023-12-12T10:11:46.362416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:46.500313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

(PDT)(LOW)제작사
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
627 
Rosemount
69 
ROSEMOUNT
 
43
Honeywell
 
10
Yokogawa
 
3

Length

Max length9
Median length4
Mean length4.8271277
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 627
83.4%
Rosemount 69
 
9.2%
ROSEMOUNT 43
 
5.7%
Honeywell 10
 
1.3%
Yokogawa 3
 
0.4%

Length

2023-12-12T10:11:46.658675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:11:47.086519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 627
83.4%
rosemount 112
 
14.9%
honeywell 10
 
1.3%
yokogawa 3
 
0.4%

(PDT)(HIGH)제작사
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
627 
Rosemount
79 
ROSEMOUNT
 
43
Yokogawa
 
3

Length

Max length9
Median length4
Mean length4.8271277
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 627
83.4%
Rosemount 79
 
10.5%
ROSEMOUNT 43
 
5.7%
Yokogawa 3
 
0.4%

Length

2023-12-12T10:11:47.234965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:11:47.398419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 627
83.4%
rosemount 122
 
16.2%
yokogawa 3
 
0.4%

(PDT)최근교정년월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
<NA>
627 
2022-04-01
 
27
2022-10-01
 
13
2022-06-01
 
12
44866
 
12
Other values (10)
 
61

Length

Max length10
Median length4
Mean length4.7114362
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 627
83.4%
2022-04-01 27
 
3.6%
2022-10-01 13
 
1.7%
2022-06-01 12
 
1.6%
44866 12
 
1.6%
44652 11
 
1.5%
2022-05-01 10
 
1.3%
2022-03-01 10
 
1.3%
2022-11-01 9
 
1.2%
44743 6
 
0.8%
Other values (5) 15
 
2.0%

Length

2023-12-12T10:11:47.561638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 627
83.4%
2022-04-01 27
 
3.6%
2022-10-01 13
 
1.7%
2022-06-01 12
 
1.6%
44866 12
 
1.6%
44652 11
 
1.5%
2022-05-01 10
 
1.3%
2022-03-01 10
 
1.3%
2022-11-01 9
 
1.2%
44743 6
 
0.8%
Other values (5) 15
 
2.0%

Interactions

2023-12-12T10:11:41.111184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:11:47.707274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역본부(유랑계)수요처(유량계)TYPE(유량계)SIZE(유량계)최근교정년월(TT)제작사(TT)최근교정년월(RTD)제작사(RTD)최근교정년월(PT)제작사(PT)최근교정년월(PDT)(LOW)제작사(PDT)(HIGH)제작사(PDT)최근교정년월
지역본부1.0000.9940.3220.4600.7690.7700.6730.6850.9220.7700.6730.8400.9580.820
(유랑계)수요처0.9941.0000.8420.8160.9520.8920.9170.9350.9780.8980.9170.9831.0000.948
(유량계)TYPE0.3220.8421.0000.4650.8460.5410.3720.6230.4180.5530.3720.1280.0740.000
(유량계)SIZE0.4600.8160.4651.0000.7390.1920.3500.2740.3630.2440.3500.3990.5900.485
(유량계)최근교정년월0.7690.9520.8460.7391.0000.7570.6790.7890.7470.7210.6790.7680.7860.740
(TT)제작사0.7700.8920.5410.1920.7571.0000.5770.8600.6310.9910.5770.8760.4400.791
(TT)최근교정년월0.6730.9170.3720.3500.6790.5771.0000.4160.7660.6111.0000.6950.5680.968
(RTD)제작사0.6850.9350.6230.2740.7890.8600.4161.0000.6410.8960.4160.5550.8690.543
(RTD)최근교정년월0.9220.9780.4180.3630.7470.6310.7660.6411.0000.7290.7660.6980.6160.778
(PT)제작사0.7700.8980.5530.2440.7210.9910.6110.8960.7291.0000.6111.0001.0000.821
(PT)최근교정년월0.6730.9170.3720.3500.6790.5771.0000.4160.7660.6111.0000.6950.5680.968
(PDT)(LOW)제작사0.8400.9830.1280.3990.7680.8760.6950.5550.6981.0000.6951.0001.0000.630
(PDT)(HIGH)제작사0.9581.0000.0740.5900.7860.4400.5680.8690.6161.0000.5681.0001.0000.657
(PDT)최근교정년월0.8200.9480.0000.4850.7400.7910.9680.5430.7780.8210.9680.6300.6571.000
2023-12-12T10:11:47.974332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(유량계)TYPE(PDT)(HIGH)제작사(PDT)최근교정년월지역본부(PT)제작사(TT)제작사(PDT)(LOW)제작사(RTD)제작사
(유량계)TYPE1.0000.1220.0000.1970.2430.2360.0830.317
(PDT)(HIGH)제작사0.1221.0000.4500.7380.9960.6810.9960.562
(PDT)최근교정년월0.0000.4501.0000.5170.6350.6070.3930.343
지역본부0.1970.7380.5171.0000.5810.5820.7070.414
(PT)제작사0.2430.9960.6350.5811.0000.8720.9920.592
(TT)제작사0.2360.6810.6070.5820.8721.0000.6750.537
(PDT)(LOW)제작사0.0830.9960.3930.7070.9920.6751.0000.560
(RTD)제작사0.3170.5620.3430.4140.5920.5370.5601.000
2023-12-12T10:11:48.132256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(유량계)SIZE지역본부(유량계)TYPE(TT)제작사(RTD)제작사(PT)제작사(PDT)(LOW)제작사(PDT)(HIGH)제작사(PDT)최근교정년월
(유량계)SIZE1.0000.1560.2950.1150.1340.1470.2590.3100.221
지역본부0.1561.0000.1970.5820.4140.5810.7070.7380.517
(유량계)TYPE0.2950.1971.0000.2360.3170.2430.0830.1220.000
(TT)제작사0.1150.5820.2361.0000.5370.8720.6750.6810.607
(RTD)제작사0.1340.4140.3170.5371.0000.5920.5600.5620.343
(PT)제작사0.1470.5810.2430.8720.5921.0000.9920.9960.635
(PDT)(LOW)제작사0.2590.7070.0830.6750.5600.9921.0000.9960.393
(PDT)(HIGH)제작사0.3100.7380.1220.6810.5620.9960.9961.0000.450
(PDT)최근교정년월0.2210.5170.0000.6070.3430.6350.3930.4501.000

Missing values

2023-12-12T10:11:41.243006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:11:41.461864image/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.
2023-12-12T10:11:41.595272image/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

지역본부관리소(유랑계)수요처(유량계)TYPE(유량계)SIZE(유량계)최근교정년월(TT)제작사(TT)최근교정년월(RTD)제작사(RTD)최근교정년월(PT)제작사(PT)최근교정년월(PDT)(LOW)제작사(PDT)(HIGH)제작사(PDT)최근교정년월
0서울고척귀뚜라미에너지터빈102019-06-01ROSEMOUNT2022-05-01WISE2022-04-01ROSEMOUNT2022-05-01<NA><NA><NA>
1서울고척귀뚜라미에너지터빈162017-02-01ROSEMOUNT2022-05-01WISE2022-04-01ROSEMOUNT2022-05-01<NA><NA><NA>
2서울고척귀뚜라미에너지터빈162017-02-01ROSEMOUNT2022-05-01WISE2022-04-01ROSEMOUNT2022-05-01<NA><NA><NA>
3서울고척삼천리터빈82021-05-01ROSEMOUNT2022-05-01WISE2022-04-01ROSEMOUNT2022-05-01<NA><NA><NA>
4서울고척삼천리터빈102019-07-01ROSEMOUNT2022-05-01WISE2022-04-01ROSEMOUNT2022-05-01<NA><NA><NA>
5서울고척삼천리터빈102018-07-01ROSEMOUNT2022-05-01WISE2022-04-01ROSEMOUNT2022-05-01<NA><NA><NA>
6서울교하서울도시가스터빈162022-11-01ROSEMOUNT2022-10-01WISE2022-04-01ROSEMOUNT2022-10-01<NA><NA><NA>
7서울교하서울도시가스터빈162017-12-01ROSEMOUNT2022-10-01WISE2022-04-01ROSEMOUNT2022-10-01<NA><NA><NA>
8서울교하서울도시가스터빈162017-01-01ROSEMOUNT2022-10-01WISE2022-04-01ROSEMOUNT2022-10-01<NA><NA><NA>
9서울교하지역난방공사초음파42020-04-01ROSEMOUNT2022-10-01WISE2022-04-01ROSEMOUNT2022-10-01<NA><NA><NA>
지역본부관리소(유랑계)수요처(유량계)TYPE(유량계)SIZE(유량계)최근교정년월(TT)제작사(TT)최근교정년월(RTD)제작사(RTD)최근교정년월(PT)제작사(PT)최근교정년월(PDT)(LOW)제작사(PDT)(HIGH)제작사(PDT)최근교정년월
742제주LNG봉개제주도시가스터빈102019-08-01ROSEMOUNT2022-07-01ROSEMOUNT2022-08-01ROSEMOUNT2022-07-01<NA><NA><NA>
743제주LNG봉개중부발전 제주복합화력초음파42019-05-01ROSEMOUNT2022-07-01HISCO2022-08-01ROSEMOUNT2022-07-01<NA><NA><NA>
744제주LNG봉개중부발전 제주복합화력초음파82019-04-01ROSEMOUNT2022-07-01HISCO2022-08-01ROSEMOUNT2022-07-01<NA><NA><NA>
745제주LNG봉개중부발전 제주복합화력초음파82019-05-01ROSEMOUNT2022-07-01HISCO2022-08-01ROSEMOUNT2022-07-01<NA><NA><NA>
746제주LNG한림남부발전 한림복합화력초음파42019-04-01ROSEMOUNT2022-07-01HISCO2022-08-01ROSEMOUNT2022-07-01<NA><NA><NA>
747제주LNG한림남부발전 한림복합화력초음파42019-04-01ROSEMOUNT2022-07-01HISCO2022-08-01ROSEMOUNT2022-07-01<NA><NA><NA>
748제주LNG한림남부발전 한림복합화력초음파42020-05-01ROSEMOUNT2022-07-01HISCO2022-08-01ROSEMOUNT2022-07-01<NA><NA><NA>
749제주LNG서귀포제주도시가스터빈42019-08-01ROSEMOUNT2022-11-01ROSEMOUNT2022-08-01ROSEMOUNT2022-11-01<NA><NA><NA>
750제주LNG서귀포제주도시가스터빈62019-08-01ROSEMOUNT2022-11-01ROSEMOUNT2022-08-01ROSEMOUNT2022-11-01<NA><NA><NA>
751제주LNG서귀포제주도시가스터빈62019-08-01ROSEMOUNT2022-11-01ROSEMOUNT2022-08-01ROSEMOUNT2022-11-01<NA><NA><NA>

Duplicate rows

Most frequently occurring

지역본부관리소(유랑계)수요처(유량계)TYPE(유량계)SIZE(유량계)최근교정년월(TT)제작사(TT)최근교정년월(RTD)제작사(RTD)최근교정년월(PT)제작사(PT)최근교정년월(PDT)(LOW)제작사(PDT)(HIGH)제작사(PDT)최근교정년월# duplicates
53서울대치코원에너지오리피스162022-03-01ROSEMOUNT2022-05-01WISE2022-04-01ROSEMOUNT2022-05-01ROSEMOUNTROSEMOUNT2022-05-014
77전북보령중부발전 보령복합오리피스162021-05-01Rosemount2022-11-01Rosemount2022-05-01Rosemount2022-11-01RosemountRosemount2022-11-014
9경기분당남동발전 분당복합오리피스102022-05-01Rosemount2022-04-01WISE2022-03-01Rosemount2022-04-01RosemountRosemount2022-04-013
20광주전남여천대화도시가스초음파102021-08-01Yokogawa2021-11-01Rosemount2022-10-01Yokogawa2021-11-01<NA><NA><NA>3
25대구경북금호대성에너지초음파162019-09-01Yokogawa2022-07-01WOOJIN2022-04-01Yokogawa2022-07-01<NA><NA><NA>3
26대구경북금호서라벌도시가스초음파62019-09-01Yokogawa2022-07-01WOOJIN2022-04-01Yokogawa2022-07-01<NA><NA><NA>3
27대구경북동김천영남에너지서비스초음파102021-04-01Rosemount2022-07-01HISCO2022-04-01Rosemount2022-07-01<NA><NA><NA>3
35대전충청아산LH열병합오리피스102022-08-01Rosemount2022-07-01Rosemount2022-02-01Rosemount2022-07-01RosemountRosemount447433
37대전충청조치원충청에너지서비스터빈102018-01-01YOKOGAWA2022-10-01Wise2022-02-01YOKOGAWA2022-10-01<NA><NA><NA>3
38대전충청청주충청에너지서비스터빈202019-08-01YOKOGAWA2022-10-01HISCO2022-02-01YOKOGAWA2022-10-01<NA><NA><NA>3