Overview

Dataset statistics

Number of variables17
Number of observations746
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory105.0 KiB
Average record size in memory144.2 B

Variable types

Text6
Numeric8
Categorical2
DateTime1

Dataset

Description충청남도 아산시 대기배출시설, 폐수배출시설 현황 데이터로 사업장명, 소재지주소, 업종, 종별, 대기오염물질 발생량(톤), 폐수배출량(톤) 등의 정보 확인이 가능합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=373&beforeMenuCd=DOM_000000201001001000&publicdatapk=15076029

Alerts

배출조업시간 is highly overall correlated with 방지조업시간High correlation
방지조업시간 is highly overall correlated with 배출조업시간 and 1 other fieldsHigh correlation
배출연간가동일수 is highly overall correlated with 방지연간가동일수High correlation
방지연간가동일수 is highly overall correlated with 방지조업시간 and 1 other fieldsHigh correlation
인허가구분 is highly imbalanced (84.9%)Imbalance
굴뚝수 has 350 (46.9%) zerosZeros
배출조업시간 has 12 (1.6%) zerosZeros
방지조업시간 has 109 (14.6%) zerosZeros
배출연간가동일수 has 14 (1.9%) zerosZeros
방지연간가동일수 has 106 (14.2%) zerosZeros
연료사용량(총합) has 499 (66.9%) zerosZeros
오염물질발생량(총합) has 85 (11.4%) zerosZeros

Reproduction

Analysis started2024-01-09 20:51:31.029301
Analysis finished2024-01-09 20:51:38.264052
Duration7.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct719
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-01-10T05:51:38.439603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length8.1434316
Min length2

Characters and Unicode

Total characters6075
Distinct characters402
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique695 ?
Unique (%)93.2%

Sample

1st row(주)광일
2nd row현대병원
3rd row(주)파라다이스 도고지점
4th row효성오앤비(주)
5th row아일수지공업(주)
ValueCountFrequency (%)
아산공장 23
 
2.7%
주식회사 17
 
2.0%
아산지점 6
 
0.7%
제2공장 4
 
0.5%
주)한미에프쓰리 3
 
0.4%
한일산업(주 3
 
0.4%
아산사업장 3
 
0.4%
이든테크(주 3
 
0.4%
주)세명테크 3
 
0.4%
주)나라켐 2
 
0.2%
Other values (746) 777
92.1%
2024-01-10T05:51:38.779389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
569
 
9.4%
( 559
 
9.2%
) 559
 
9.2%
180
 
3.0%
163
 
2.7%
160
 
2.6%
129
 
2.1%
111
 
1.8%
104
 
1.7%
98
 
1.6%
Other values (392) 3443
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4812
79.2%
Open Punctuation 559
 
9.2%
Close Punctuation 559
 
9.2%
Space Separator 98
 
1.6%
Decimal Number 18
 
0.3%
Uppercase Letter 16
 
0.3%
Other Symbol 9
 
0.1%
Other Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
569
 
11.8%
180
 
3.7%
163
 
3.4%
160
 
3.3%
129
 
2.7%
111
 
2.3%
104
 
2.2%
96
 
2.0%
84
 
1.7%
78
 
1.6%
Other values (372) 3138
65.2%
Uppercase Letter
ValueCountFrequency (%)
E 4
25.0%
N 2
12.5%
G 2
12.5%
C 2
12.5%
B 2
12.5%
A 1
 
6.2%
M 1
 
6.2%
K 1
 
6.2%
R 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 10
55.6%
1 6
33.3%
3 1
 
5.6%
4 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
/ 1
25.0%
: 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 559
100.0%
Close Punctuation
ValueCountFrequency (%)
) 559
100.0%
Space Separator
ValueCountFrequency (%)
98
100.0%
Other Symbol
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4821
79.4%
Common 1238
 
20.4%
Latin 16
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
569
 
11.8%
180
 
3.7%
163
 
3.4%
160
 
3.3%
129
 
2.7%
111
 
2.3%
104
 
2.2%
96
 
2.0%
84
 
1.7%
78
 
1.6%
Other values (373) 3147
65.3%
Common
ValueCountFrequency (%)
( 559
45.2%
) 559
45.2%
98
 
7.9%
2 10
 
0.8%
1 6
 
0.5%
. 2
 
0.2%
3 1
 
0.1%
/ 1
 
0.1%
4 1
 
0.1%
: 1
 
0.1%
Latin
ValueCountFrequency (%)
E 4
25.0%
N 2
12.5%
G 2
12.5%
C 2
12.5%
B 2
12.5%
A 1
 
6.2%
M 1
 
6.2%
K 1
 
6.2%
R 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4812
79.2%
ASCII 1254
 
20.6%
None 9
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
569
 
11.8%
180
 
3.7%
163
 
3.4%
160
 
3.3%
129
 
2.7%
111
 
2.3%
104
 
2.2%
96
 
2.0%
84
 
1.7%
78
 
1.6%
Other values (372) 3138
65.2%
ASCII
ValueCountFrequency (%)
( 559
44.6%
) 559
44.6%
98
 
7.8%
2 10
 
0.8%
1 6
 
0.5%
E 4
 
0.3%
N 2
 
0.2%
G 2
 
0.2%
C 2
 
0.2%
B 2
 
0.2%
Other values (9) 10
 
0.8%
None
ValueCountFrequency (%)
9
100.0%
Distinct707
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-01-10T05:51:39.022097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length35
Mean length22.713137
Min length1

Characters and Unicode

Total characters16944
Distinct characters196
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique675 ?
Unique (%)90.5%

Sample

1st row충청남도 아산시 풍기동 226
2nd row충청남도 아산시 온천동 220-16
3rd row충청남도 아산시 도고면 기곡리 180-1-1
4th row충청남도 아산시 신동 279-10
5th row충청남도 아산시 실옥동 242
ValueCountFrequency (%)
충청남도 744
19.7%
아산시 744
19.7%
둔포면 202
 
5.4%
음봉면 126
 
3.3%
영인면 98
 
2.6%
인주면 54
 
1.4%
신창면 54
 
1.4%
석곡리 45
 
1.2%
산동리 39
 
1.0%
선장면 38
 
1.0%
Other values (843) 1627
43.1%
2024-01-10T05:51:39.408128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3776
22.3%
844
 
5.0%
799
 
4.7%
769
 
4.5%
748
 
4.4%
748
 
4.4%
746
 
4.4%
746
 
4.4%
680
 
4.0%
1 664
 
3.9%
Other values (186) 6424
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9777
57.7%
Space Separator 3776
 
22.3%
Decimal Number 2844
 
16.8%
Dash Punctuation 520
 
3.1%
Close Punctuation 13
 
0.1%
Open Punctuation 13
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
844
 
8.6%
799
 
8.2%
769
 
7.9%
748
 
7.7%
748
 
7.7%
746
 
7.6%
746
 
7.6%
680
 
7.0%
618
 
6.3%
231
 
2.4%
Other values (171) 2848
29.1%
Decimal Number
ValueCountFrequency (%)
1 664
23.3%
2 395
13.9%
3 343
12.1%
5 256
 
9.0%
4 228
 
8.0%
6 217
 
7.6%
8 199
 
7.0%
0 193
 
6.8%
9 184
 
6.5%
7 165
 
5.8%
Space Separator
ValueCountFrequency (%)
3776
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 520
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9777
57.7%
Common 7166
42.3%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
844
 
8.6%
799
 
8.2%
769
 
7.9%
748
 
7.7%
748
 
7.7%
746
 
7.6%
746
 
7.6%
680
 
7.0%
618
 
6.3%
231
 
2.4%
Other values (171) 2848
29.1%
Common
ValueCountFrequency (%)
3776
52.7%
1 664
 
9.3%
- 520
 
7.3%
2 395
 
5.5%
3 343
 
4.8%
5 256
 
3.6%
4 228
 
3.2%
6 217
 
3.0%
8 199
 
2.8%
0 193
 
2.7%
Other values (4) 375
 
5.2%
Latin
ValueCountFrequency (%)
I 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9777
57.7%
ASCII 7167
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3776
52.7%
1 664
 
9.3%
- 520
 
7.3%
2 395
 
5.5%
3 343
 
4.8%
5 256
 
3.6%
4 228
 
3.2%
6 217
 
3.0%
8 199
 
2.8%
0 193
 
2.7%
Other values (5) 376
 
5.2%
Hangul
ValueCountFrequency (%)
844
 
8.6%
799
 
8.2%
769
 
7.9%
748
 
7.7%
748
 
7.7%
746
 
7.6%
746
 
7.6%
680
 
7.0%
618
 
6.3%
231
 
2.4%
Other values (171) 2848
29.1%
Distinct645
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-01-10T05:51:39.657999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length35
Mean length21.650134
Min length1

Characters and Unicode

Total characters16151
Distinct characters215
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

Unique615 ?
Unique (%)82.4%

Sample

1st row충청남도 아산시 어의정로 157 (풍기동)
2nd row충청남도 아산시 시민로 388 (온천동)
3rd row
4th row충청남도 아산시 온천대로 1785 (신동)
5th row충청남도 아산시 실옥로 110-29 (실옥동)
ValueCountFrequency (%)
충청남도 678
19.6%
아산시 678
19.6%
둔포면 194
 
5.6%
음봉면 109
 
3.2%
영인면 90
 
2.6%
신창면 47
 
1.4%
인주면 46
 
1.3%
선장면 36
 
1.0%
염치읍 30
 
0.9%
아산호로 24
 
0.7%
Other values (778) 1525
44.1%
2024-01-10T05:51:40.026738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2881
17.8%
909
 
5.6%
814
 
5.0%
729
 
4.5%
710
 
4.4%
689
 
4.3%
684
 
4.2%
678
 
4.2%
576
 
3.6%
555
 
3.4%
Other values (205) 6926
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9984
61.8%
Space Separator 2881
 
17.8%
Decimal Number 2788
 
17.3%
Dash Punctuation 297
 
1.8%
Open Punctuation 99
 
0.6%
Close Punctuation 99
 
0.6%
Uppercase Letter 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
909
 
9.1%
814
 
8.2%
729
 
7.3%
710
 
7.1%
689
 
6.9%
684
 
6.9%
678
 
6.8%
576
 
5.8%
555
 
5.6%
287
 
2.9%
Other values (186) 3353
33.6%
Decimal Number
ValueCountFrequency (%)
1 530
19.0%
2 428
15.4%
3 306
11.0%
4 252
9.0%
0 241
8.6%
6 233
8.4%
7 219
7.9%
5 213
7.6%
9 187
 
6.7%
8 179
 
6.4%
Open Punctuation
ValueCountFrequency (%)
( 98
99.0%
[ 1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 98
99.0%
] 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
2881
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 297
100.0%
Other Punctuation
ValueCountFrequency (%)
* 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9984
61.8%
Common 6165
38.2%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
909
 
9.1%
814
 
8.2%
729
 
7.3%
710
 
7.1%
689
 
6.9%
684
 
6.9%
678
 
6.8%
576
 
5.8%
555
 
5.6%
287
 
2.9%
Other values (186) 3353
33.6%
Common
ValueCountFrequency (%)
2881
46.7%
1 530
 
8.6%
2 428
 
6.9%
3 306
 
5.0%
- 297
 
4.8%
4 252
 
4.1%
0 241
 
3.9%
6 233
 
3.8%
7 219
 
3.6%
5 213
 
3.5%
Other values (7) 565
 
9.2%
Latin
ValueCountFrequency (%)
A 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9984
61.8%
ASCII 6167
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2881
46.7%
1 530
 
8.6%
2 428
 
6.9%
3 306
 
5.0%
- 297
 
4.8%
4 252
 
4.1%
0 241
 
3.9%
6 233
 
3.8%
7 219
 
3.6%
5 213
 
3.5%
Other values (9) 567
 
9.2%
Hangul
ValueCountFrequency (%)
909
 
9.1%
814
 
8.2%
729
 
7.3%
710
 
7.1%
689
 
6.9%
684
 
6.9%
678
 
6.8%
576
 
5.8%
555
 
5.6%
287
 
2.9%
Other values (186) 3353
33.6%
Distinct235
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-01-10T05:51:40.296405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length22
Mean length12.788204
Min length1

Characters and Unicode

Total characters9540
Distinct characters250
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)16.8%

Sample

1st row식품 첨가물 제조업
2nd row병원
3rd row숙박업
4th row기타 비료 및 질소화합물 제조업
5th row플라스틱제품 제조업
ValueCountFrequency (%)
제조업 456
 
17.6%
기타 232
 
9.0%
227
 
8.8%
자동차 109
 
4.2%
그외 97
 
3.7%
부품 66
 
2.5%
플라스틱 48
 
1.9%
수리업 39
 
1.5%
플라스틱제품 36
 
1.4%
생산업 34
 
1.3%
Other values (356) 1245
48.1%
2024-01-10T05:51:40.910965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2023
21.2%
702
 
7.4%
629
 
6.6%
534
 
5.6%
408
 
4.3%
281
 
2.9%
247
 
2.6%
240
 
2.5%
164
 
1.7%
164
 
1.7%
Other values (240) 4148
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7514
78.8%
Space Separator 2023
 
21.2%
Other Punctuation 2
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
702
 
9.3%
629
 
8.4%
534
 
7.1%
408
 
5.4%
281
 
3.7%
247
 
3.3%
240
 
3.2%
164
 
2.2%
164
 
2.2%
162
 
2.2%
Other values (237) 3983
53.0%
Space Separator
ValueCountFrequency (%)
2023
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7514
78.8%
Common 2026
 
21.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
702
 
9.3%
629
 
8.4%
534
 
7.1%
408
 
5.4%
281
 
3.7%
247
 
3.3%
240
 
3.2%
164
 
2.2%
164
 
2.2%
162
 
2.2%
Other values (237) 3983
53.0%
Common
ValueCountFrequency (%)
2023
99.9%
· 2
 
0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7514
78.8%
ASCII 2024
 
21.2%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2023
> 99.9%
1 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
702
 
9.3%
629
 
8.4%
534
 
7.1%
408
 
5.4%
281
 
3.7%
247
 
3.3%
240
 
3.2%
164
 
2.2%
164
 
2.2%
162
 
2.2%
Other values (237) 3983
53.0%
None
ValueCountFrequency (%)
· 2
100.0%
Distinct423
Distinct (%)56.9%
Missing2
Missing (%)0.3%
Memory size6.0 KiB
2024-01-10T05:51:41.191689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length33
Mean length5.3051075
Min length1

Characters and Unicode

Total characters3947
Distinct characters396
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique395 ?
Unique (%)53.1%

Sample

1st row식품첨가물 등
2nd row
3rd row
4th row유기질비료
5th row물탱크
ValueCountFrequency (%)
자동차부품 27
 
3.3%
26
 
3.2%
자동차 24
 
3.0%
18
 
2.2%
부품 15
 
1.9%
15
 
1.9%
플라스틱 13
 
1.6%
레미콘 10
 
1.2%
자동차용 9
 
1.1%
반도체 8
 
1.0%
Other values (536) 645
79.6%
2024-01-10T05:51:41.608524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
599
 
15.2%
136
 
3.4%
106
 
2.7%
104
 
2.6%
102
 
2.6%
101
 
2.6%
59
 
1.5%
55
 
1.4%
55
 
1.4%
54
 
1.4%
Other values (386) 2576
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2825
71.6%
Space Separator 599
 
15.2%
Uppercase Letter 273
 
6.9%
Lowercase Letter 145
 
3.7%
Close Punctuation 29
 
0.7%
Open Punctuation 29
 
0.7%
Decimal Number 24
 
0.6%
Other Punctuation 19
 
0.5%
Dash Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
136
 
4.8%
106
 
3.8%
104
 
3.7%
102
 
3.6%
101
 
3.6%
59
 
2.1%
55
 
1.9%
55
 
1.9%
54
 
1.9%
48
 
1.7%
Other values (326) 2005
71.0%
Uppercase Letter
ValueCountFrequency (%)
P 31
11.4%
E 28
 
10.3%
R 25
 
9.2%
C 22
 
8.1%
L 19
 
7.0%
A 18
 
6.6%
S 18
 
6.6%
D 16
 
5.9%
T 15
 
5.5%
O 11
 
4.0%
Other values (13) 70
25.6%
Lowercase Letter
ValueCountFrequency (%)
e 21
14.5%
r 17
11.7%
a 16
11.0%
t 12
8.3%
l 10
 
6.9%
n 9
 
6.2%
i 9
 
6.2%
o 8
 
5.5%
c 7
 
4.8%
m 7
 
4.8%
Other values (11) 29
20.0%
Decimal Number
ValueCountFrequency (%)
0 10
41.7%
3 3
 
12.5%
1 3
 
12.5%
5 2
 
8.3%
8 2
 
8.3%
6 1
 
4.2%
4 1
 
4.2%
9 1
 
4.2%
2 1
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 9
47.4%
/ 6
31.6%
: 4
21.1%
Space Separator
ValueCountFrequency (%)
599
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2825
71.6%
Common 704
 
17.8%
Latin 418
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
136
 
4.8%
106
 
3.8%
104
 
3.7%
102
 
3.6%
101
 
3.6%
59
 
2.1%
55
 
1.9%
55
 
1.9%
54
 
1.9%
48
 
1.7%
Other values (326) 2005
71.0%
Latin
ValueCountFrequency (%)
P 31
 
7.4%
E 28
 
6.7%
R 25
 
6.0%
C 22
 
5.3%
e 21
 
5.0%
L 19
 
4.5%
A 18
 
4.3%
S 18
 
4.3%
r 17
 
4.1%
a 16
 
3.8%
Other values (34) 203
48.6%
Common
ValueCountFrequency (%)
599
85.1%
) 29
 
4.1%
( 29
 
4.1%
0 10
 
1.4%
. 9
 
1.3%
/ 6
 
0.9%
- 4
 
0.6%
: 4
 
0.6%
3 3
 
0.4%
1 3
 
0.4%
Other values (6) 8
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2825
71.6%
ASCII 1122
 
28.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
599
53.4%
P 31
 
2.8%
) 29
 
2.6%
( 29
 
2.6%
E 28
 
2.5%
R 25
 
2.2%
C 22
 
2.0%
e 21
 
1.9%
L 19
 
1.7%
A 18
 
1.6%
Other values (50) 301
26.8%
Hangul
ValueCountFrequency (%)
136
 
4.8%
106
 
3.8%
104
 
3.7%
102
 
3.6%
101
 
3.6%
59
 
2.1%
55
 
1.9%
55
 
1.9%
54
 
1.9%
48
 
1.7%
Other values (326) 2005
71.0%

굴뚝수
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.772118
Minimum0
Maximum25
Zeros350
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-01-10T05:51:41.725015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7.75
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8499994
Coefficient of variation (CV)1.6082448
Kurtosis12.585026
Mean1.772118
Median Absolute Deviation (MAD)1
Skewness2.9447782
Sum1322
Variance8.1224967
MonotonicityNot monotonic
2024-01-10T05:51:41.837018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 350
46.9%
1 124
 
16.6%
2 101
 
13.5%
3 46
 
6.2%
4 32
 
4.3%
5 29
 
3.9%
6 15
 
2.0%
8 12
 
1.6%
7 11
 
1.5%
10 6
 
0.8%
Other values (9) 20
 
2.7%
ValueCountFrequency (%)
0 350
46.9%
1 124
 
16.6%
2 101
 
13.5%
3 46
 
6.2%
4 32
 
4.3%
5 29
 
3.9%
6 15
 
2.0%
7 11
 
1.5%
8 12
 
1.6%
9 5
 
0.7%
ValueCountFrequency (%)
25 1
 
0.1%
20 1
 
0.1%
18 1
 
0.1%
16 2
 
0.3%
15 1
 
0.1%
13 2
 
0.3%
12 4
0.5%
11 3
0.4%
10 6
0.8%
9 5
0.7%

배출시설수
Real number (ℝ)

Distinct43
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0268097
Minimum0
Maximum97
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-01-10T05:51:41.947862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q39
95-th percentile22
Maximum97
Range97
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.4476632
Coefficient of variation (CV)1.3445167
Kurtosis31.343442
Mean7.0268097
Median Absolute Deviation (MAD)3
Skewness4.5533094
Sum5242
Variance89.258341
MonotonicityNot monotonic
2024-01-10T05:51:42.066546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2 139
18.6%
1 134
18.0%
3 67
9.0%
4 59
7.9%
5 53
 
7.1%
6 39
 
5.2%
7 35
 
4.7%
9 26
 
3.5%
8 25
 
3.4%
12 22
 
2.9%
Other values (33) 147
19.7%
ValueCountFrequency (%)
0 1
 
0.1%
1 134
18.0%
2 139
18.6%
3 67
9.0%
4 59
7.9%
5 53
 
7.1%
6 39
 
5.2%
7 35
 
4.7%
8 25
 
3.4%
9 26
 
3.5%
ValueCountFrequency (%)
97 2
0.3%
75 1
0.1%
74 1
0.1%
57 2
0.3%
55 1
0.1%
45 1
0.1%
44 1
0.1%
38 1
0.1%
37 1
0.1%
34 1
0.1%


Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
5종
451 
4종
270 
3종
 
19
2종
 
6

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5종
2nd row5종
3rd row4종
4th row4종
5th row4종

Common Values

ValueCountFrequency (%)
5종 451
60.5%
4종 270
36.2%
3종 19
 
2.5%
2종 6
 
0.8%

Length

2024-01-10T05:51:42.171328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:51:42.265220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5종 451
60.5%
4종 270
36.2%
3종 19
 
2.5%
2종 6
 
0.8%

배출조업시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.918499
Minimum0
Maximum25
Zeros12
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-01-10T05:51:42.359490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.25
Q18
median8
Q310
95-th percentile24
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.3266439
Coefficient of variation (CV)0.57944266
Kurtosis0.26828822
Mean10.918499
Median Absolute Deviation (MAD)0
Skewness1.2727517
Sum8145.2
Variance40.026422
MonotonicityNot monotonic
2024-01-10T05:51:42.477511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8.0 486
65.1%
24.0 111
 
14.9%
10.0 24
 
3.2%
12.0 22
 
2.9%
20.0 19
 
2.5%
6.0 13
 
1.7%
0.0 12
 
1.6%
4.0 11
 
1.5%
16.0 9
 
1.2%
3.0 6
 
0.8%
Other values (13) 33
 
4.4%
ValueCountFrequency (%)
0.0 12
 
1.6%
0.2 1
 
0.1%
1.0 1
 
0.1%
2.0 3
 
0.4%
3.0 6
 
0.8%
4.0 11
 
1.5%
5.0 4
 
0.5%
6.0 13
 
1.7%
7.0 2
 
0.3%
8.0 486
65.1%
ValueCountFrequency (%)
25.0 1
 
0.1%
24.0 111
14.9%
23.0 2
 
0.3%
22.0 5
 
0.7%
21.0 2
 
0.3%
20.0 19
 
2.5%
18.0 4
 
0.5%
16.0 9
 
1.2%
15.0 2
 
0.3%
14.0 1
 
0.1%

방지조업시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.508311
Minimum0
Maximum335
Zeros109
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-01-10T05:51:42.589324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median8
Q38
95-th percentile24
Maximum335
Range335
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.29201
Coefficient of variation (CV)2.1977169
Kurtosis124.83828
Mean11.508311
Median Absolute Deviation (MAD)0
Skewness10.806921
Sum8585.2
Variance639.68576
MonotonicityNot monotonic
2024-01-10T05:51:42.697871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8.0 419
56.2%
0.0 109
 
14.6%
24.0 97
 
13.0%
10.0 22
 
2.9%
12.0 17
 
2.3%
20.0 16
 
2.1%
6.0 13
 
1.7%
4.0 7
 
0.9%
16.0 7
 
0.9%
5.0 5
 
0.7%
Other values (14) 34
 
4.6%
ValueCountFrequency (%)
0.0 109
 
14.6%
0.2 1
 
0.1%
2.0 2
 
0.3%
3.0 4
 
0.5%
4.0 7
 
0.9%
5.0 5
 
0.7%
6.0 13
 
1.7%
7.0 2
 
0.3%
8.0 419
56.2%
9.0 4
 
0.5%
ValueCountFrequency (%)
335.0 1
 
0.1%
300.0 4
 
0.5%
25.0 1
 
0.1%
24.0 97
13.0%
23.0 2
 
0.3%
22.0 4
 
0.5%
21.0 2
 
0.3%
20.0 16
 
2.1%
18.0 4
 
0.5%
16.0 7
 
0.9%

배출연간가동일수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.41957
Minimum0
Maximum365
Zeros14
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-01-10T05:51:42.815390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.25
Q1300
median300
Q3300
95-th percentile360
Maximum365
Range365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation56.189489
Coefficient of variation (CV)0.19755845
Kurtosis13.818104
Mean284.41957
Median Absolute Deviation (MAD)0
Skewness-3.3056022
Sum212177
Variance3157.2586
MonotonicityNot monotonic
2024-01-10T05:51:42.933421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
300 505
67.7%
240 57
 
7.6%
365 31
 
4.2%
250 27
 
3.6%
264 20
 
2.7%
260 15
 
2.0%
0 14
 
1.9%
360 9
 
1.2%
280 8
 
1.1%
288 7
 
0.9%
Other values (32) 53
 
7.1%
ValueCountFrequency (%)
0 14
1.9%
8 2
 
0.3%
25 1
 
0.1%
30 1
 
0.1%
35 1
 
0.1%
100 1
 
0.1%
120 4
 
0.5%
122 1
 
0.1%
150 2
 
0.3%
170 1
 
0.1%
ValueCountFrequency (%)
365 31
4.2%
360 9
 
1.2%
350 5
 
0.7%
341 1
 
0.1%
340 1
 
0.1%
336 2
 
0.3%
335 1
 
0.1%
332 1
 
0.1%
330 2
 
0.3%
324 1
 
0.1%

방지연간가동일수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.9437
Minimum0
Maximum365
Zeros106
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-01-10T05:51:43.054374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1250
median300
Q3300
95-th percentile347.75
Maximum365
Range365
Interquartile range (IQR)50

Descriptive statistics

Standard deviation107.1476
Coefficient of variation (CV)0.42868695
Kurtosis1.4154856
Mean249.9437
Median Absolute Deviation (MAD)0
Skewness-1.7301976
Sum186458
Variance11480.609
MonotonicityNot monotonic
2024-01-10T05:51:43.170646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
300 449
60.2%
0 106
 
14.2%
240 50
 
6.7%
365 23
 
3.1%
250 21
 
2.8%
264 19
 
2.5%
260 14
 
1.9%
360 10
 
1.3%
280 8
 
1.1%
350 5
 
0.7%
Other values (29) 41
 
5.5%
ValueCountFrequency (%)
0 106
14.2%
25 1
 
0.1%
30 1
 
0.1%
35 1
 
0.1%
100 1
 
0.1%
120 2
 
0.3%
150 1
 
0.1%
170 1
 
0.1%
180 1
 
0.1%
200 4
 
0.5%
ValueCountFrequency (%)
365 23
3.1%
360 10
1.3%
350 5
 
0.7%
341 1
 
0.1%
340 1
 
0.1%
336 1
 
0.1%
335 1
 
0.1%
332 1
 
0.1%
330 2
 
0.3%
320 1
 
0.1%
Distinct684
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Minimum1972-08-01 00:00:00
Maximum2022-06-13 00:00:00
2024-01-10T05:51:43.312992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:43.473146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
신고
718 
허가
 
27
 
1

Length

Max length2
Median length2
Mean length1.9986595
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
신고 718
96.2%
허가 27
 
3.6%
1
 
0.1%

Length

2024-01-10T05:51:43.640460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:51:43.754209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신고 718
96.4%
허가 27
 
3.6%
Distinct255
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2024-01-10T05:51:44.019591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length1
Mean length4.4865952
Min length1

Characters and Unicode

Total characters3347
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)29.9%

Sample

1st row2015-01-22
2nd row2004-03-29
3rd row2011-07-29
4th row2021-04-20
5th row
ValueCountFrequency (%)
2021-10-21 3
 
1.0%
2021-09-30 3
 
1.0%
2022-01-19 3
 
1.0%
2021-12-08 3
 
1.0%
2022-05-19 2
 
0.7%
2018-07-02 2
 
0.7%
2019-12-17 2
 
0.7%
2020-01-22 2
 
0.7%
2021-01-18 2
 
0.7%
2020-11-16 2
 
0.7%
Other values (244) 265
91.7%
2024-01-10T05:51:44.397710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 661
19.7%
2 651
19.5%
- 578
17.3%
1 503
15.0%
457
13.7%
9 97
 
2.9%
8 87
 
2.6%
3 78
 
2.3%
7 73
 
2.2%
6 60
 
1.8%
Other values (2) 102
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2312
69.1%
Dash Punctuation 578
 
17.3%
Space Separator 457
 
13.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 661
28.6%
2 651
28.2%
1 503
21.8%
9 97
 
4.2%
8 87
 
3.8%
3 78
 
3.4%
7 73
 
3.2%
6 60
 
2.6%
4 55
 
2.4%
5 47
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 578
100.0%
Space Separator
ValueCountFrequency (%)
457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 661
19.7%
2 651
19.5%
- 578
17.3%
1 503
15.0%
457
13.7%
9 97
 
2.9%
8 87
 
2.6%
3 78
 
2.3%
7 73
 
2.2%
6 60
 
1.8%
Other values (2) 102
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 661
19.7%
2 651
19.5%
- 578
17.3%
1 503
15.0%
457
13.7%
9 97
 
2.9%
8 87
 
2.6%
3 78
 
2.3%
7 73
 
2.2%
6 60
 
1.8%
Other values (2) 102
 
3.0%

연료사용량(총합)
Real number (ℝ)

ZEROS 

Distinct226
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326465.69
Minimum0
Maximum34992000
Zeros499
Zeros (%)66.9%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-01-10T05:51:44.528735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q321450
95-th percentile1316378.4
Maximum34992000
Range34992000
Interquartile range (IQR)21450

Descriptive statistics

Standard deviation1971921.8
Coefficient of variation (CV)6.0402114
Kurtosis198.25403
Mean326465.69
Median Absolute Deviation (MAD)0
Skewness13.196389
Sum2.4354341 × 108
Variance3.8884756 × 1012
MonotonicityNot monotonic
2024-01-10T05:51:44.650794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 499
66.9%
12000.0 5
 
0.7%
60000.0 5
 
0.7%
24000.0 3
 
0.4%
144000.0 3
 
0.4%
22500.0 2
 
0.3%
21000.0 2
 
0.3%
15000.0 2
 
0.3%
90000.0 2
 
0.3%
135000.0 2
 
0.3%
Other values (216) 221
29.6%
ValueCountFrequency (%)
0.0 499
66.9%
1.65 1
 
0.1%
25.0 1
 
0.1%
29.6 1
 
0.1%
40.0 1
 
0.1%
80.0 1
 
0.1%
120.0 1
 
0.1%
132.0 1
 
0.1%
150.0 1
 
0.1%
157.0 1
 
0.1%
ValueCountFrequency (%)
34992000.0 1
0.1%
27939456.0 1
0.1%
20700000.0 1
0.1%
15120000.0 1
0.1%
6350400.0 1
0.1%
5586607.2 1
0.1%
4200000.0 1
0.1%
4146120.0 1
0.1%
3906011.0 1
0.1%
3759240.0 1
0.1%

오염물질발생량(총합)
Real number (ℝ)

ZEROS 

Distinct441
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.815265
Minimum0
Maximum4220
Zeros85
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-01-10T05:51:44.774426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1925
median1.2
Q33.94
95-th percentile9.7475
Maximum4220
Range4220
Interquartile range (IQR)3.7475

Descriptive statistics

Standard deviation210.57693
Coefficient of variation (CV)14.213511
Kurtosis368.99303
Mean14.815265
Median Absolute Deviation (MAD)1.19
Skewness19.176539
Sum11052.188
Variance44342.645
MonotonicityNot monotonic
2024-01-10T05:51:44.902167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 85
 
11.4%
0.07 8
 
1.1%
0.28 7
 
0.9%
0.12 7
 
0.9%
0.01 7
 
0.9%
0.04 6
 
0.8%
0.74 6
 
0.8%
0.03 6
 
0.8%
0.15 5
 
0.7%
0.17 5
 
0.7%
Other values (431) 604
81.0%
ValueCountFrequency (%)
0.0 85
11.4%
0.002 2
 
0.3%
0.004 4
 
0.5%
0.005 3
 
0.4%
0.006 1
 
0.1%
0.01 7
 
0.9%
0.018 1
 
0.1%
0.02 4
 
0.5%
0.025 1
 
0.1%
0.03 6
 
0.8%
ValueCountFrequency (%)
4220.0 1
0.1%
3900.0 1
0.1%
331.73 1
0.1%
171.7 1
0.1%
103.2 1
0.1%
62.6 1
0.1%
51.61 1
0.1%
43.28 1
0.1%
42.2 1
0.1%
38.79 1
0.1%

Interactions

2024-01-10T05:51:37.314043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.065159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.779313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:33.529796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.448588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.147589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.880319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.571580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.390626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.154192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.866944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:33.617070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.529181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.232418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.962280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.655334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.465400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.235296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.969539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:33.700914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.611488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.322493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.046508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.746228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.544443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.323041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:33.067770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.023849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.691048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.407047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.127415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.847168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.639881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.414561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:33.163100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.102155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.784739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.514437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.218630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.957186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.722654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.509471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:33.259685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.190048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.871065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.618022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.309992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.059011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.808632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.602345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:33.349049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.273439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.972540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.708923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.397226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.147376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.897044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:32.695262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:33.448899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:34.367038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.063492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:35.797971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:36.489126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:37.237372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:51:44.986996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
굴뚝수배출시설수배출조업시간방지조업시간배출연간가동일수방지연간가동일수인허가구분연료사용량(총합)오염물질발생량(총합)
굴뚝수1.0000.7230.4580.2940.0000.0000.1130.3320.7160.000
배출시설수0.7231.0000.5890.1790.0000.0000.0000.3600.2920.000
0.4580.5891.0000.3140.0000.1800.2240.1790.4170.041
배출조업시간0.2940.1790.3141.0000.0000.5670.4240.2670.1420.165
방지조업시간0.0000.0000.0000.0001.0000.3260.0880.0000.0000.000
배출연간가동일수0.0000.0000.1800.5670.3261.0000.9890.3250.0000.119
방지연간가동일수0.1130.0000.2240.4240.0880.9891.0000.3750.0000.000
인허가구분0.3320.3600.1790.2670.0000.3250.3751.0000.2690.000
연료사용량(총합)0.7160.2920.4170.1420.0000.0000.0000.2691.0000.000
오염물질발생량(총합)0.0000.0000.0410.1650.0000.1190.0000.0000.0001.000
2024-01-10T05:51:45.094685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가구분
1.0000.170
인허가구분0.1701.000
2024-01-10T05:51:45.173200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
굴뚝수배출시설수배출조업시간방지조업시간배출연간가동일수방지연간가동일수연료사용량(총합)오염물질발생량(총합)인허가구분
굴뚝수1.0000.2660.1260.292-0.0560.1420.0790.3050.2760.217
배출시설수0.2661.0000.2750.237-0.0110.0060.1280.3370.2950.245
배출조업시간0.1260.2751.0000.7090.1520.1380.1180.1710.2230.165
방지조업시간0.2920.2370.7091.0000.1590.5290.1590.2520.0000.000
배출연간가동일수-0.056-0.0110.1520.1591.0000.6860.1310.0750.1150.150
방지연간가동일수0.1420.0060.1380.5290.6861.0000.1430.1710.1440.178
연료사용량(총합)0.0790.1280.1180.1590.1310.1431.0000.3150.2800.115
오염물질발생량(총합)0.3050.3370.1710.2520.0750.1710.3151.0000.0270.000
0.2760.2950.2230.0000.1150.1440.2800.0271.0000.170
인허가구분0.2170.2450.1650.0000.1500.1780.1150.0000.1701.000

Missing values

2024-01-10T05:51:38.013717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:51:38.191262image/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

사업장명소재지도로명소재지대표업종주생산품명굴뚝수배출시설수배출조업시간방지조업시간배출연간가동일수방지연간가동일수인허가등록일자인허가구분최종변경일자연료사용량(총합)오염물질발생량(총합)
0(주)광일충청남도 아산시 풍기동 226충청남도 아산시 어의정로 157 (풍기동)식품 첨가물 제조업식품첨가물 등465종8.08.03003001972-08-01신고2015-01-220.00.21
1현대병원충청남도 아산시 온천동 220-16충청남도 아산시 시민로 388 (온천동)병원015종6.06.03653651973-09-07신고2004-03-2991323.01.203
2(주)파라다이스 도고지점충청남도 아산시 도고면 기곡리 180-1-1숙박업024종24.024.03603601981-08-28신고2011-07-29806130.08.85
3효성오앤비(주)충청남도 아산시 신동 279-10충청남도 아산시 온천대로 1785 (신동)기타 비료 및 질소화합물 제조업유기질비료034종8.08.03003001982-09-16신고2021-04-200.03.66
4아일수지공업(주)충청남도 아산시 실옥동 242충청남도 아산시 실옥로 110-29 (실옥동)플라스틱제품 제조업물탱크0114종8.08.03003001986-06-19신고0.03.56
5뉴 코리아호텔충청남도 아산시 온천동 230-6숙박업015종0.00.0001986-10-13신고0.00.0
6한라엔컴(주)충청남도 아산시 배방읍 갈매리 114-1 레미콘공장충청남도 아산시 배방읍 봉강천로 131 레미콘공장비금속광물제품 제조업레미콘0124종8.08.03003001986-05-22신고86427.04.87
7우진특수도장충청남도 아산시 염치읍 석정리 31-11충청남도 아산시 염치읍 충무로 215-1기타 조립금속제품 제조업자동차휠카바054종8.08.03003001986-08-02신고2017-07-1387600.03.22
8(주)경보제약충청남도 아산시 실옥동 345-6충청남도 아산시 실옥로 174 (실옥동)1972종24.024.03653651987-04-14허가2019-01-312383886.434.7
9한일산업(주)아산공장충청남도 아산시 염치읍 염성리 155충청남도 아산시 염치읍 아산로 505콘크리트 시멘트 및 플라스터 제품 제조업레미콘574종8.08.03003001987-07-07신고2019-06-260.07.74
사업장명소재지도로명소재지대표업종주생산품명굴뚝수배출시설수배출조업시간방지조업시간배출연간가동일수방지연간가동일수인허가등록일자인허가구분최종변경일자연료사용량(총합)오염물질발생량(총합)
736(주)탑머티리얼충청남도 아산시 둔포면 석곡리 1939충청남도 아산시 둔포면 아산밸리로388번길 82축전지 제조업285종24.02.02642642022-04-20신고0.00.3
737(주)티케이케미칼 이엔에이치공장충청남도 아산시 둔포면 석곡리 1342충청남도 아산시 둔포면 아산밸리북로 112반도체 및 평판디스플레이 제조용 기계 제조업2155종24.024.03503502022-04-24신고2022-04-120.00.46
738천안아산자동차검사소충청남도 아산시 배방읍 갈매리 16-10충청남도 아산시 배방읍 호서로 460-4자동차 종합 수리업도색차량115종4.04.03003002022-04-27신고0.00.427
739(주)오딘솔루션충청남도 아산시 음봉면 산동리 180-1 남일수출포장충청남도 아산시 음봉면 산동로 321-9 남일수출포장125종8.08.03003002022-05-02신고0.00.74
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