Overview

Dataset statistics

Number of variables7
Number of observations252
Missing cells664
Missing cells (%)37.6%
Duplicate rows1
Duplicate rows (%)0.4%
Total size in memory13.9 KiB
Average record size in memory56.5 B

Variable types

Text7

Dataset

Description국내 석유제품의 제주 지역 월간 산업별 소비량(농림수산업,광업,식품.담배업,섬유제품업,목재업,제지.인쇄업,화학제품업,요업,철강업,비철금속산업,기계조립업,수송장비업,기타제조업,건설업,기타에너지,발전,석유정제,개스제조,철도,도로,해운,항공,상업,가정,공공,기타), 제품별 단위 : 물량(KL)
URLhttps://www.data.go.kr/data/15122211/fileData.do

Alerts

Dataset has 1 (0.4%) duplicate rowsDuplicates
2023-01 has 100 (39.7%) missing valuesMissing
2023-02 has 102 (40.5%) missing valuesMissing
2023-03 has 104 (41.3%) missing valuesMissing
2023-04 has 113 (44.8%) missing valuesMissing
2023-05 has 119 (47.2%) missing valuesMissing
2023-06 has 126 (50.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:16:46.161233
Analysis finished2023-12-12 19:16:48.021144
Duration1.86 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구_산업_제품
Text

Distinct251
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-13T04:16:48.247952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length16.400794
Min length10

Characters and Unicode

Total characters4133
Distinct characters95
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

Unique250 ?
Unique (%)99.2%

Sample

1st row대전동구_농림수산업_무연보통휘발유
2nd row대전동구_농림수산업_실내등유
3rd row대전동구_농림수산업_경유(0.001%)
4th row대전동구_광업_경유(0.001%)
5th row대전동구_식품.담배업_실내등유
ValueCountFrequency (%)
대전동구_식품.담배업_실내등유 2
 
0.8%
대전유성구_가정_실내등유 1
 
0.4%
대전유성구_공공_부생연료유(등유형 1
 
0.4%
대전유성구_가정_용제원료 1
 
0.4%
대전유성구_도로_경유(0.001 1
 
0.4%
대전유성구_도로_부탄 1
 
0.4%
대전유성구_해운_경유(0.05 1
 
0.4%
대전유성구_해운_경질중유(0.3 1
 
0.4%
대전유성구_상업_무연보통휘발유 1
 
0.4%
대전유성구_상업_실내등유 1
 
0.4%
Other values (241) 241
95.6%
2023-12-13T04:16:48.747012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 504
 
12.2%
대 317
 
7.7%
유 267
 
6.5%
전 258
 
6.2%
구 252
 
6.1%
0 190
 
4.6%
업 159
 
3.8%
) 104
 
2.5%
( 104
 
2.5%
. 99
 
2.4%
Other values (85) 1879
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2942
71.2%
Connector Punctuation 504
 
12.2%
Decimal Number 272
 
6.6%
Other Punctuation 178
 
4.3%
Close Punctuation 104
 
2.5%
Open Punctuation 104
 
2.5%
Uppercase Letter 26
 
0.6%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 317
 
10.8%
유 267
 
9.1%
전 258
 
8.8%
구 252
 
8.6%
업 159
 
5.4%
제 78
 
2.7%
덕 65
 
2.2%
경 64
 
2.2%
연 60
 
2.0%
성 59
 
2.0%
Other values (70) 1363
46.3%
Uppercase Letter
ValueCountFrequency (%)
C 14
53.8%
T 3
 
11.5%
A 3
 
11.5%
E 3
 
11.5%
J 3
 
11.5%
Decimal Number
ValueCountFrequency (%)
0 190
69.9%
1 57
 
21.0%
3 15
 
5.5%
5 10
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 99
55.6%
% 79
44.4%
Connector Punctuation
ValueCountFrequency (%)
_ 504
100.0%
Close Punctuation
ValueCountFrequency (%)
) 104
100.0%
Open Punctuation
ValueCountFrequency (%)
( 104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2942
71.2%
Common 1165
 
28.2%
Latin 26
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 317
 
10.8%
유 267
 
9.1%
전 258
 
8.8%
구 252
 
8.6%
업 159
 
5.4%
제 78
 
2.7%
덕 65
 
2.2%
경 64
 
2.2%
연 60
 
2.0%
성 59
 
2.0%
Other values (70) 1363
46.3%
Common
ValueCountFrequency (%)
_ 504
43.3%
0 190
 
16.3%
) 104
 
8.9%
( 104
 
8.9%
. 99
 
8.5%
% 79
 
6.8%
1 57
 
4.9%
3 15
 
1.3%
5 10
 
0.9%
- 3
 
0.3%
Latin
ValueCountFrequency (%)
C 14
53.8%
T 3
 
11.5%
A 3
 
11.5%
E 3
 
11.5%
J 3
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2942
71.2%
ASCII 1191
28.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 504
42.3%
0 190
 
16.0%
) 104
 
8.7%
( 104
 
8.7%
. 99
 
8.3%
% 79
 
6.6%
1 57
 
4.8%
3 15
 
1.3%
C 14
 
1.2%
5 10
 
0.8%
Other values (5) 15
 
1.3%
Hangul
ValueCountFrequency (%)
대 317
 
10.8%
유 267
 
9.1%
전 258
 
8.8%
구 252
 
8.6%
업 159
 
5.4%
제 78
 
2.7%
덕 65
 
2.2%
경 64
 
2.2%
연 60
 
2.0%
성 59
 
2.0%
Other values (70) 1363
46.3%

2023-01
Text

MISSING 

Distinct95
Distinct (%)62.5%
Missing100
Missing (%)39.7%
Memory size2.1 KiB
2023-12-13T04:16:49.046863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.2763158
Min length1

Characters and Unicode

Total characters346
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

Unique77 ?
Unique (%)50.7%

Sample

1st row0
2nd row65
3rd row3
4th row81
5th row6
ValueCountFrequency (%)
1 11
 
7.2%
4 8
 
5.3%
3 7
 
4.6%
2 6
 
3.9%
0 6
 
3.9%
10 5
 
3.3%
7 4
 
2.6%
15 4
 
2.6%
6 3
 
2.0%
54 3
 
2.0%
Other values (85) 95
62.5%
2023-12-13T04:16:49.519316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 63
18.2%
4 43
12.4%
3 42
12.1%
5 42
12.1%
2 34
9.8%
8 31
9.0%
0 26
7.5%
6 24
 
6.9%
, 19
 
5.5%
7 12
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 327
94.5%
Other Punctuation 19
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 63
19.3%
4 43
13.1%
3 42
12.8%
5 42
12.8%
2 34
10.4%
8 31
9.5%
0 26
8.0%
6 24
 
7.3%
7 12
 
3.7%
9 10
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 63
18.2%
4 43
12.4%
3 42
12.1%
5 42
12.1%
2 34
9.8%
8 31
9.0%
0 26
7.5%
6 24
 
6.9%
, 19
 
5.5%
7 12
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 63
18.2%
4 43
12.4%
3 42
12.1%
5 42
12.1%
2 34
9.8%
8 31
9.0%
0 26
7.5%
6 24
 
6.9%
, 19
 
5.5%
7 12
 
3.5%

2023-02
Text

MISSING 

Distinct93
Distinct (%)62.0%
Missing102
Missing (%)40.5%
Memory size2.1 KiB
2023-12-13T04:16:49.785517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.2666667
Min length1

Characters and Unicode

Total characters340
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

Unique74 ?
Unique (%)49.3%

Sample

1st row1
2nd row50
3rd row4
4th row14
5th row6
ValueCountFrequency (%)
4 14
 
9.3%
1 9
 
6.0%
2 7
 
4.7%
0 7
 
4.7%
8 6
 
4.0%
13 4
 
2.7%
5 3
 
2.0%
3 3
 
2.0%
21 3
 
2.0%
9 2
 
1.3%
Other values (83) 92
61.3%
2023-12-13T04:16:50.285872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 60
17.6%
2 50
14.7%
4 39
11.5%
5 35
10.3%
3 33
9.7%
0 24
 
7.1%
6 24
 
7.1%
8 23
 
6.8%
9 19
 
5.6%
, 19
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 321
94.4%
Other Punctuation 19
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 60
18.7%
2 50
15.6%
4 39
12.1%
5 35
10.9%
3 33
10.3%
0 24
 
7.5%
6 24
 
7.5%
8 23
 
7.2%
9 19
 
5.9%
7 14
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 340
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 60
17.6%
2 50
14.7%
4 39
11.5%
5 35
10.3%
3 33
9.7%
0 24
 
7.1%
6 24
 
7.1%
8 23
 
6.8%
9 19
 
5.6%
, 19
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 60
17.6%
2 50
14.7%
4 39
11.5%
5 35
10.3%
3 33
9.7%
0 24
 
7.1%
6 24
 
7.1%
8 23
 
6.8%
9 19
 
5.6%
, 19
 
5.6%

2023-03
Text

MISSING 

Distinct90
Distinct (%)60.8%
Missing104
Missing (%)41.3%
Memory size2.1 KiB
2023-12-13T04:16:50.636976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.2432432
Min length1

Characters and Unicode

Total characters332
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

Unique69 ?
Unique (%)46.6%

Sample

1st row2
2nd row23
3rd row6
4th row66
5th row5
ValueCountFrequency (%)
1 10
 
6.8%
2 9
 
6.1%
4 8
 
5.4%
0 7
 
4.7%
5 5
 
3.4%
3 5
 
3.4%
7 3
 
2.0%
12 3
 
2.0%
37 3
 
2.0%
15 3
 
2.0%
Other values (80) 92
62.2%
2023-12-13T04:16:51.069367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 60
18.1%
2 52
15.7%
3 39
11.7%
7 31
9.3%
5 30
9.0%
0 29
8.7%
4 27
8.1%
6 22
 
6.6%
9 17
 
5.1%
, 16
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316
95.2%
Other Punctuation 16
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 60
19.0%
2 52
16.5%
3 39
12.3%
7 31
9.8%
5 30
9.5%
0 29
9.2%
4 27
8.5%
6 22
 
7.0%
9 17
 
5.4%
8 9
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 60
18.1%
2 52
15.7%
3 39
11.7%
7 31
9.3%
5 30
9.0%
0 29
8.7%
4 27
8.1%
6 22
 
6.6%
9 17
 
5.1%
, 16
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 60
18.1%
2 52
15.7%
3 39
11.7%
7 31
9.3%
5 30
9.0%
0 29
8.7%
4 27
8.1%
6 22
 
6.6%
9 17
 
5.1%
, 16
 
4.8%

2023-04
Text

MISSING 

Distinct85
Distinct (%)61.2%
Missing113
Missing (%)44.8%
Memory size2.1 KiB
2023-12-13T04:16:51.309168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.1870504
Min length1

Characters and Unicode

Total characters304
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

Unique67 ?
Unique (%)48.2%

Sample

1st row2
2nd row8
3rd row5
4th row89
5th row3
ValueCountFrequency (%)
2 14
 
10.1%
5 8
 
5.8%
1 7
 
5.0%
3 7
 
5.0%
0 6
 
4.3%
10 4
 
2.9%
6 3
 
2.2%
4 3
 
2.2%
16 2
 
1.4%
79 2
 
1.4%
Other values (75) 83
59.7%
2023-12-13T04:16:51.997436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 55
18.1%
2 50
16.4%
3 33
10.9%
0 25
8.2%
6 25
8.2%
5 23
7.6%
7 22
 
7.2%
4 20
 
6.6%
9 18
 
5.9%
8 17
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 288
94.7%
Other Punctuation 16
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 55
19.1%
2 50
17.4%
3 33
11.5%
0 25
8.7%
6 25
8.7%
5 23
8.0%
7 22
 
7.6%
4 20
 
6.9%
9 18
 
6.2%
8 17
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 55
18.1%
2 50
16.4%
3 33
10.9%
0 25
8.2%
6 25
8.2%
5 23
7.6%
7 22
 
7.2%
4 20
 
6.6%
9 18
 
5.9%
8 17
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 55
18.1%
2 50
16.4%
3 33
10.9%
0 25
8.2%
6 25
8.2%
5 23
7.6%
7 22
 
7.2%
4 20
 
6.6%
9 18
 
5.9%
8 17
 
5.6%

2023-05
Text

MISSING 

Distinct87
Distinct (%)65.4%
Missing119
Missing (%)47.2%
Memory size2.1 KiB
2023-12-13T04:16:52.227476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.2330827
Min length1

Characters and Unicode

Total characters297
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

Unique71 ?
Unique (%)53.4%

Sample

1st row2
2nd row0
3rd row5
4th row45
5th row3
ValueCountFrequency (%)
0 14
 
10.5%
4 8
 
6.0%
2 8
 
6.0%
1 5
 
3.8%
17 3
 
2.3%
9 3
 
2.3%
3 3
 
2.3%
11 2
 
1.5%
76 2
 
1.5%
8 2
 
1.5%
Other values (77) 83
62.4%
2023-12-13T04:16:52.600187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 49
16.5%
2 43
14.5%
8 31
10.4%
0 28
9.4%
6 27
9.1%
4 26
8.8%
3 25
8.4%
5 19
 
6.4%
7 17
 
5.7%
9 16
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 281
94.6%
Other Punctuation 16
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 49
17.4%
2 43
15.3%
8 31
11.0%
0 28
10.0%
6 27
9.6%
4 26
9.3%
3 25
8.9%
5 19
 
6.8%
7 17
 
6.0%
9 16
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 49
16.5%
2 43
14.5%
8 31
10.4%
0 28
9.4%
6 27
9.1%
4 26
8.8%
3 25
8.4%
5 19
 
6.4%
7 17
 
5.7%
9 16
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 49
16.5%
2 43
14.5%
8 31
10.4%
0 28
9.4%
6 27
9.1%
4 26
8.8%
3 25
8.4%
5 19
 
6.4%
7 17
 
5.7%
9 16
 
5.4%

2023-06
Text

MISSING 

Distinct81
Distinct (%)64.3%
Missing126
Missing (%)50.0%
Memory size2.1 KiB
2023-12-13T04:16:52.855316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.2936508
Min length1

Characters and Unicode

Total characters289
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

Unique61 ?
Unique (%)48.4%

Sample

1st row1
2nd row4
3rd row54
4th row3
5th row117
ValueCountFrequency (%)
4 8
 
6.3%
1 8
 
6.3%
0 6
 
4.8%
2 5
 
4.0%
22 3
 
2.4%
6 3
 
2.4%
11 3
 
2.4%
28 3
 
2.4%
7 3
 
2.4%
5 3
 
2.4%
Other values (71) 81
64.3%
2023-12-13T04:16:53.292861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 56
19.4%
2 44
15.2%
4 27
9.3%
3 27
9.3%
6 22
 
7.6%
8 22
 
7.6%
0 20
 
6.9%
7 20
 
6.9%
9 19
 
6.6%
5 16
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 273
94.5%
Other Punctuation 16
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 56
20.5%
2 44
16.1%
4 27
9.9%
3 27
9.9%
6 22
 
8.1%
8 22
 
8.1%
0 20
 
7.3%
7 20
 
7.3%
9 19
 
7.0%
5 16
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 289
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 56
19.4%
2 44
15.2%
4 27
9.3%
3 27
9.3%
6 22
 
7.6%
8 22
 
7.6%
0 20
 
6.9%
7 20
 
6.9%
9 19
 
6.6%
5 16
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 289
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 56
19.4%
2 44
15.2%
4 27
9.3%
3 27
9.3%
6 22
 
7.6%
8 22
 
7.6%
0 20
 
6.9%
7 20
 
6.9%
9 19
 
6.6%
5 16
 
5.5%

Correlations

2023-12-13T04:16:53.457033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-012023-022023-032023-042023-052023-06
2023-011.0000.9970.9970.9980.9970.996
2023-020.9971.0000.9970.9970.9950.994
2023-030.9970.9971.0000.9970.9990.995
2023-040.9980.9970.9971.0000.9980.997
2023-050.9970.9950.9990.9981.0000.997
2023-060.9960.9940.9950.9970.9971.000

Missing values

2023-12-13T04:16:47.641397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:16:47.796927image/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.
2023-12-13T04:16:47.923945image/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

시군구_산업_제품2023-012023-022023-032023-042023-052023-06
0대전동구_농림수산업_무연보통휘발유012221
1대전동구_농림수산업_실내등유65502380<NA>
2대전동구_농림수산업_경유(0.001%)346554
3대전동구_광업_경유(0.001%)<NA><NA><NA><NA><NA><NA>
4대전동구_식품.담배업_실내등유<NA><NA><NA><NA><NA><NA>
5대전동구_식품.담배업_실내등유<NA><NA><NA><NA><NA><NA>
6대전동구_식품.담배업_경질중유(0.5%)<NA><NA><NA><NA><NA><NA>
7대전동구_식품.담배업_경질중유(0.3%)<NA><NA><NA><NA><NA><NA>
8대전동구_식품.담배업_벙커C유(0.3%)<NA><NA><NA><NA><NA><NA>
9대전동구_제지.인쇄업_중유(0.3%)<NA><NA><NA><NA><NA><NA>
시군구_산업_제품2023-012023-022023-032023-042023-052023-06
242대전대덕구_상업_부생연료유(중유형)<NA><NA><NA><NA><NA><NA>
243대전대덕구_가정_무연보통휘발유<NA><NA><NA><NA><NA><NA>
244대전대덕구_가정_실내등유358237104657640
245대전대덕구_가정_경유(0.001%)140125255295214289
246대전대덕구_가정_용제원료152535
247대전대덕구_가정_프로판601642472472463379
248대전대덕구_가정_부탄29231350<NA>
249대전대덕구_공공_무연보통휘발유<NA><NA><NA><NA><NA><NA>
250대전대덕구_공공_실내등유742<NA><NA><NA>
251대전대덕구_공공_경유(0.001%)14111414799108134

Duplicate rows

Most frequently occurring

시군구_산업_제품2023-012023-022023-032023-042023-052023-06# duplicates
0대전동구_식품.담배업_실내등유<NA><NA><NA><NA><NA><NA>2