Overview

Dataset statistics

Number of variables15
Number of observations7713
Missing cells34860
Missing cells (%)30.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory971.8 KiB
Average record size in memory129.0 B

Variable types

Text4
Categorical3
Numeric8

Dataset

Description환경정보공개시스템 환경정보 중 환경정보 통계에 활용된 사업체별 데이터(용수사용량, 에너지사용량, 대기오염배출량, 수질오염배출량, 폐기물발생량, 화학물질 배출량, 용수 재활용량, 폐기물재활용량) 정보 사업체별 15년~19년도 정보를 제공함
Author한국환경산업기술원
URLhttps://www.data.go.kr/data/15101493/fileData.do

Alerts

용수 사용량 is highly overall correlated with 용수 재활용량 and 4 other fieldsHigh correlation
용수 재활용량 is highly overall correlated with 용수 사용량 and 3 other fieldsHigh correlation
에너지 총량 is highly overall correlated with 용수 사용량 and 4 other fieldsHigh correlation
대기오염물질 총량 is highly overall correlated with 에너지 총량High correlation
수질오염물질 총량 is highly overall correlated with 용수 사용량 and 1 other fieldsHigh correlation
폐기물 재활용량 is highly overall correlated with 용수 사용량 and 3 other fieldsHigh correlation
폐기물발생 총량 is highly overall correlated with 용수 사용량 and 4 other fieldsHigh correlation
유형 is highly overall correlated with 업종High correlation
업종 is highly overall correlated with 유형High correlation
대표사업장코드 has 6330 (82.1%) missing valuesMissing
사업장코드 has 6330 (82.1%) missing valuesMissing
용수 재활용량 has 5230 (67.8%) missing valuesMissing
대기오염물질 총량 has 4272 (55.4%) missing valuesMissing
수질오염물질 총량 has 4741 (61.5%) missing valuesMissing
폐기물 재활용량 has 2794 (36.2%) missing valuesMissing
화학물질 배출량 has 5073 (65.8%) missing valuesMissing
폐기물 재활용량 is highly skewed (γ1 = 21.00953279)Skewed
폐기물발생 총량 is highly skewed (γ1 = 25.97833398)Skewed

Reproduction

Analysis started2023-12-12 23:32:15.971675
Analysis finished2023-12-12 23:32:25.472786
Duration9.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대표사업장코드
Text

MISSING 

Distinct1383
Distinct (%)100.0%
Missing6330
Missing (%)82.1%
Memory size60.4 KiB
2023-12-13T08:32:25.644140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters27660
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1383 ?
Unique (%)100.0%

Sample

1st row00000000000000095293
2nd row00000000000000095296
3rd row00000000000000095297
4th row00000000000000095298
5th row00000000000000095299
ValueCountFrequency (%)
00000000000000095325 1
 
0.1%
00000000000000299473 1
 
0.1%
00000000000000314680 1
 
0.1%
00000000000000313836 1
 
0.1%
00000000000000313104 1
 
0.1%
00000000000000312510 1
 
0.1%
00000000000000307812 1
 
0.1%
00000000000000306315 1
 
0.1%
00000000000000305605 1
 
0.1%
00000000000000322033 1
 
0.1%
Other values (1373) 1373
99.3%
2023-12-13T08:32:26.032289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19983
72.2%
1 1221
 
4.4%
2 1027
 
3.7%
4 830
 
3.0%
8 780
 
2.8%
9 736
 
2.7%
6 718
 
2.6%
5 704
 
2.5%
7 645
 
2.3%
3 628
 
2.3%
Other values (2) 388
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27272
98.6%
Uppercase Letter 388
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19983
73.3%
1 1221
 
4.5%
2 1027
 
3.8%
4 830
 
3.0%
8 780
 
2.9%
9 736
 
2.7%
6 718
 
2.6%
5 704
 
2.6%
7 645
 
2.4%
3 628
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
C 194
50.0%
T 194
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27272
98.6%
Latin 388
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19983
73.3%
1 1221
 
4.5%
2 1027
 
3.8%
4 830
 
3.0%
8 780
 
2.9%
9 736
 
2.7%
6 718
 
2.6%
5 704
 
2.6%
7 645
 
2.4%
3 628
 
2.3%
Latin
ValueCountFrequency (%)
C 194
50.0%
T 194
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19983
72.2%
1 1221
 
4.4%
2 1027
 
3.7%
4 830
 
3.0%
8 780
 
2.8%
9 736
 
2.7%
6 718
 
2.6%
5 704
 
2.5%
7 645
 
2.3%
3 628
 
2.3%
Other values (2) 388
 
1.4%

사업장코드
Text

MISSING 

Distinct1383
Distinct (%)100.0%
Missing6330
Missing (%)82.1%
Memory size60.4 KiB
2023-12-13T08:32:26.244398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters27660
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1383 ?
Unique (%)100.0%

Sample

1st row00000000000000095293
2nd row00000000000000095296
3rd row00000000000000095297
4th row00000000000000095298
5th row00000000000000095299
ValueCountFrequency (%)
00000000000000095325 1
 
0.1%
00000000000000299473 1
 
0.1%
00000000000000314680 1
 
0.1%
00000000000000313836 1
 
0.1%
00000000000000313104 1
 
0.1%
00000000000000312510 1
 
0.1%
00000000000000307812 1
 
0.1%
00000000000000306315 1
 
0.1%
00000000000000305605 1
 
0.1%
00000000000000322033 1
 
0.1%
Other values (1373) 1373
99.3%
2023-12-13T08:32:26.541468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19983
72.2%
1 1221
 
4.4%
2 1027
 
3.7%
4 830
 
3.0%
8 780
 
2.8%
9 736
 
2.7%
6 718
 
2.6%
5 704
 
2.5%
7 645
 
2.3%
3 628
 
2.3%
Other values (2) 388
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27272
98.6%
Uppercase Letter 388
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19983
73.3%
1 1221
 
4.5%
2 1027
 
3.8%
4 830
 
3.0%
8 780
 
2.9%
9 736
 
2.7%
6 718
 
2.6%
5 704
 
2.6%
7 645
 
2.4%
3 628
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
C 194
50.0%
T 194
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27272
98.6%
Latin 388
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19983
73.3%
1 1221
 
4.5%
2 1027
 
3.8%
4 830
 
3.0%
8 780
 
2.9%
9 736
 
2.7%
6 718
 
2.6%
5 704
 
2.6%
7 645
 
2.4%
3 628
 
2.3%
Latin
ValueCountFrequency (%)
C 194
50.0%
T 194
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19983
72.2%
1 1221
 
4.4%
2 1027
 
3.7%
4 830
 
3.0%
8 780
 
2.8%
9 736
 
2.7%
6 718
 
2.6%
5 704
 
2.5%
7 645
 
2.3%
3 628
 
2.3%
Other values (2) 388
 
1.4%
Distinct1852
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
2023-12-13T08:32:26.777850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length22
Mean length8.6126021
Min length2

Characters and Unicode

Total characters66429
Distinct characters524
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)2.0%

Sample

1st rowLS산전(주) 천안사업장
2nd row한국지엠(주) 부평공장 (본사)
3rd rowROHMKOREA 대전공장
4th row(주)만도 brake 평택사업본부
5th row에스케이하이이엔지㈜
ValueCountFrequency (%)
본사 383
 
3.9%
주식회사 280
 
2.8%
본부 65
 
0.7%
부산광역시 55
 
0.6%
48
 
0.5%
서울특별시 34
 
0.3%
중구청 30
 
0.3%
대구광역시 30
 
0.3%
울산광역시 29
 
0.3%
경상북도 23
 
0.2%
Other values (2025) 8915
90.1%
2023-12-13T08:32:27.126270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3844
 
5.8%
) 3181
 
4.8%
( 3175
 
4.8%
2214
 
3.3%
1851
 
2.8%
1848
 
2.8%
1370
 
2.1%
1358
 
2.0%
1316
 
2.0%
1208
 
1.8%
Other values (514) 45064
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 56496
85.0%
Close Punctuation 3181
 
4.8%
Open Punctuation 3175
 
4.8%
Space Separator 2214
 
3.3%
Uppercase Letter 955
 
1.4%
Lowercase Letter 149
 
0.2%
Decimal Number 104
 
0.2%
Connector Punctuation 67
 
0.1%
Other Punctuation 58
 
0.1%
Dash Punctuation 19
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3844
 
6.8%
1851
 
3.3%
1848
 
3.3%
1370
 
2.4%
1358
 
2.4%
1316
 
2.3%
1208
 
2.1%
1138
 
2.0%
1112
 
2.0%
990
 
1.8%
Other values (458) 40461
71.6%
Uppercase Letter
ValueCountFrequency (%)
S 199
20.8%
K 116
12.1%
C 103
10.8%
L 75
 
7.9%
G 61
 
6.4%
D 39
 
4.1%
P 37
 
3.9%
I 34
 
3.6%
N 33
 
3.5%
T 29
 
3.0%
Other values (14) 229
24.0%
Lowercase Letter
ValueCountFrequency (%)
k 21
14.1%
t 16
10.7%
e 15
10.1%
s 14
9.4%
p 12
8.1%
a 12
8.1%
b 11
7.4%
o 10
6.7%
m 8
 
5.4%
r 6
 
4.0%
Other values (7) 24
16.1%
Decimal Number
ValueCountFrequency (%)
1 74
71.2%
2 15
 
14.4%
3 13
 
12.5%
5 2
 
1.9%
Other Punctuation
ValueCountFrequency (%)
& 35
60.3%
. 13
 
22.4%
, 6
 
10.3%
/ 4
 
6.9%
Close Punctuation
ValueCountFrequency (%)
) 3181
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3175
100.0%
Space Separator
ValueCountFrequency (%)
2214
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 67
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Other Symbol
ValueCountFrequency (%)
6
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 56502
85.1%
Common 8823
 
13.3%
Latin 1104
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3844
 
6.8%
1851
 
3.3%
1848
 
3.3%
1370
 
2.4%
1358
 
2.4%
1316
 
2.3%
1208
 
2.1%
1138
 
2.0%
1112
 
2.0%
990
 
1.8%
Other values (459) 40467
71.6%
Latin
ValueCountFrequency (%)
S 199
18.0%
K 116
 
10.5%
C 103
 
9.3%
L 75
 
6.8%
G 61
 
5.5%
D 39
 
3.5%
P 37
 
3.4%
I 34
 
3.1%
N 33
 
3.0%
T 29
 
2.6%
Other values (31) 378
34.2%
Common
ValueCountFrequency (%)
) 3181
36.1%
( 3175
36.0%
2214
25.1%
1 74
 
0.8%
_ 67
 
0.8%
& 35
 
0.4%
- 19
 
0.2%
2 15
 
0.2%
. 13
 
0.1%
3 13
 
0.1%
Other values (4) 17
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 56496
85.0%
ASCII 9927
 
14.9%
None 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3844
 
6.8%
1851
 
3.3%
1848
 
3.3%
1370
 
2.4%
1358
 
2.4%
1316
 
2.3%
1208
 
2.1%
1138
 
2.0%
1112
 
2.0%
990
 
1.8%
Other values (458) 40461
71.6%
ASCII
ValueCountFrequency (%)
) 3181
32.0%
( 3175
32.0%
2214
22.3%
S 199
 
2.0%
K 116
 
1.2%
C 103
 
1.0%
L 75
 
0.8%
1 74
 
0.7%
_ 67
 
0.7%
G 61
 
0.6%
Other values (45) 662
 
6.7%
None
ValueCountFrequency (%)
6
100.0%

유형
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
제조
3189 
공공행정
2337 
기타서비스
1137 
기타산업
499 
교육서비스
370 

Length

Max length5
Median length4
Mean length3.3215351
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제조
2nd row제조
3rd row제조
4th row제조
5th row기타서비스

Common Values

ValueCountFrequency (%)
제조 3189
41.3%
공공행정 2337
30.3%
기타서비스 1137
 
14.7%
기타산업 499
 
6.5%
교육서비스 370
 
4.8%
보건 181
 
2.3%

Length

2023-12-13T08:32:27.235781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:32:27.326408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조 3189
41.3%
공공행정 2337
30.3%
기타서비스 1137
 
14.7%
기타산업 499
 
6.5%
교육서비스 370
 
4.8%
보건 181
 
2.3%

업종
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
제조업
3154 
공공행정, 국방 및 사회보장 행정
1910 
교육 서비스업
423 
운수업
375 
전문, 과학 및 기술 서비스업
360 
Other values (15)
1491 

Length

Max length24
Median length23
Mean length9.9111889
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제조업
2nd row제조업
3rd row제조업
4th row제조업
5th row사업시설관리 및 사업지원 서비스업

Common Values

ValueCountFrequency (%)
제조업 3154
40.9%
공공행정, 국방 및 사회보장 행정 1910
24.8%
교육 서비스업 423
 
5.5%
운수업 375
 
4.9%
전문, 과학 및 기술 서비스업 360
 
4.7%
전기, 가스, 증기 및 수도사업 287
 
3.7%
사업시설관리 및 사업지원 서비스업 244
 
3.2%
보건업 및 사회복지 서비스업 197
 
2.6%
하수·폐기물 처리, 원료재생 및 환경복원업 151
 
2.0%
금융 및 보험업 109
 
1.4%
Other values (10) 503
 
6.5%

Length

2023-12-13T08:32:27.435532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3693
17.0%
제조업 3154
14.5%
국방 1910
 
8.8%
사회보장 1910
 
8.8%
행정 1910
 
8.8%
공공행정 1910
 
8.8%
서비스업 1318
 
6.1%
교육 423
 
1.9%
운수업 375
 
1.7%
전문 360
 
1.7%
Other values (40) 4802
22.1%
Distinct62
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
2023-12-13T08:32:27.685534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length14.440685
Min length2

Characters and Unicode

Total characters111381
Distinct characters156
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

Unique0 ?
Unique (%)0.0%

Sample

1st row전기장비 제조업
2nd row자동차 및 트레일러 제조업
3rd row전자부품, 컴퓨터, 영상, 음향 및 통신장비 제조업
4th row자동차 및 트레일러 제조업
5th row사업지원 서비스업
ValueCountFrequency (%)
4731
 
15.7%
제조업 2520
 
8.3%
공공행정 1910
 
6.3%
행정 1910
 
6.3%
국방 1910
 
6.3%
사회보장 1910
 
6.3%
서비스업 928
 
3.1%
제외 694
 
2.3%
화학물질 448
 
1.5%
제조업;의약품 448
 
1.5%
Other values (127) 12791
42.4%
2023-12-13T08:32:28.046077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22487
20.2%
6035
 
5.4%
5870
 
5.3%
4731
 
4.2%
4565
 
4.1%
3904
 
3.5%
, 3883
 
3.5%
3820
 
3.4%
3556
 
3.2%
2888
 
2.6%
Other values (146) 49642
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83774
75.2%
Space Separator 22487
 
20.2%
Other Punctuation 4691
 
4.2%
Decimal Number 429
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6035
 
7.2%
5870
 
7.0%
4731
 
5.6%
4565
 
5.4%
3904
 
4.7%
3820
 
4.6%
3556
 
4.2%
2888
 
3.4%
2404
 
2.9%
2185
 
2.6%
Other values (141) 43816
52.3%
Other Punctuation
ValueCountFrequency (%)
, 3883
82.8%
; 802
 
17.1%
· 6
 
0.1%
Space Separator
ValueCountFrequency (%)
22487
100.0%
Decimal Number
ValueCountFrequency (%)
1 429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83774
75.2%
Common 27607
 
24.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6035
 
7.2%
5870
 
7.0%
4731
 
5.6%
4565
 
5.4%
3904
 
4.7%
3820
 
4.6%
3556
 
4.2%
2888
 
3.4%
2404
 
2.9%
2185
 
2.6%
Other values (141) 43816
52.3%
Common
ValueCountFrequency (%)
22487
81.5%
, 3883
 
14.1%
; 802
 
2.9%
1 429
 
1.6%
· 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83774
75.2%
ASCII 27601
 
24.8%
None 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22487
81.5%
, 3883
 
14.1%
; 802
 
2.9%
1 429
 
1.6%
Hangul
ValueCountFrequency (%)
6035
 
7.2%
5870
 
7.0%
4731
 
5.6%
4565
 
5.4%
3904
 
4.7%
3820
 
4.6%
3556
 
4.2%
2888
 
3.4%
2404
 
2.9%
2185
 
2.6%
Other values (141) 43816
52.3%
None
ValueCountFrequency (%)
· 6
100.0%

년도
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
2019
1683 
2018
1608 
2017
1539 
2016
1500 
2015
1383 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2019 1683
21.8%
2018 1608
20.8%
2017 1539
20.0%
2016 1500
19.4%
2015 1383
17.9%

Length

2023-12-13T08:32:28.166645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:32:28.249148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 1683
21.8%
2018 1608
20.8%
2017 1539
20.0%
2016 1500
19.4%
2015 1383
17.9%

용수 사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct7574
Distinct (%)98.5%
Missing24
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1290496.6
Minimum2.343754
Maximum1.5555751 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-12-13T08:32:28.350088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.343754
5-th percentile2381.2
Q126859
median104096
Q3508028
95-th percentile4357392.4
Maximum1.5555751 × 108
Range1.5555751 × 108
Interquartile range (IQR)481169

Descriptive statistics

Standard deviation6748827.7
Coefficient of variation (CV)5.2296361
Kurtosis214.12097
Mean1290496.6
Median Absolute Deviation (MAD)96792
Skewness13.123238
Sum9.9226285 × 109
Variance4.5546675 × 1013
MonotonicityNot monotonic
2023-12-13T08:32:28.480832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400.0 4
 
0.1%
720.0 3
 
< 0.1%
4980.0 3
 
< 0.1%
3960.0 3
 
< 0.1%
2536.0 3
 
< 0.1%
2458.0 3
 
< 0.1%
22200.0 3
 
< 0.1%
25398.0 2
 
< 0.1%
423657.0 2
 
< 0.1%
11788.0 2
 
< 0.1%
Other values (7564) 7661
99.3%
(Missing) 24
 
0.3%
ValueCountFrequency (%)
2.343754023 1
< 0.1%
2.39 1
< 0.1%
2.7 1
< 0.1%
3.1 1
< 0.1%
3.3 1
< 0.1%
3.5 1
< 0.1%
3.6 1
< 0.1%
4.0 2
< 0.1%
7.3 1
< 0.1%
10.0 1
< 0.1%
ValueCountFrequency (%)
155557513.0 1
< 0.1%
141852477.0 1
< 0.1%
139691178.0 1
< 0.1%
139130623.0 1
< 0.1%
137810643.0 1
< 0.1%
118128758.0 1
< 0.1%
112945469.0 1
< 0.1%
111531071.0 1
< 0.1%
110209590.0 1
< 0.1%
107363825.0 1
< 0.1%

용수 재활용량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2323
Distinct (%)93.6%
Missing5230
Missing (%)67.8%
Infinite0
Infinite (%)0.0%
Mean905688
Minimum0.01
Maximum1.0599063 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-12-13T08:32:28.630244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile36.1
Q14243
median44876
Q3260035
95-th percentile2277946.5
Maximum1.0599063 × 108
Range1.0599063 × 108
Interquartile range (IQR)255792

Descriptive statistics

Standard deviation5600418.4
Coefficient of variation (CV)6.1836067
Kurtosis178.9732
Mean905688
Median Absolute Deviation (MAD)44518.9
Skewness12.521966
Sum2.2488233 × 109
Variance3.1364686 × 1013
MonotonicityNot monotonic
2023-12-13T08:32:28.778986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 9
 
0.1%
1.0 9
 
0.1%
7200.0 7
 
0.1%
5.0 6
 
0.1%
300.0 5
 
0.1%
12.0 5
 
0.1%
20.0 5
 
0.1%
11.0 4
 
0.1%
1000.0 4
 
0.1%
110000.0 4
 
0.1%
Other values (2313) 2425
31.4%
(Missing) 5230
67.8%
ValueCountFrequency (%)
0.01 2
< 0.1%
0.1 3
< 0.1%
0.22 2
< 0.1%
0.5 2
< 0.1%
0.575 1
 
< 0.1%
0.6 1
 
< 0.1%
0.7 1
 
< 0.1%
0.741 1
 
< 0.1%
0.8 1
 
< 0.1%
0.9 2
< 0.1%
ValueCountFrequency (%)
105990630.0 1
< 0.1%
91488259.0 1
< 0.1%
89059228.0 1
< 0.1%
87046278.0 1
< 0.1%
85819933.0 1
< 0.1%
65875056.0 1
< 0.1%
64932943.0 1
< 0.1%
61811636.0 1
< 0.1%
53753775.0 1
< 0.1%
53545086.0 1
< 0.1%

에너지 총량
Real number (ℝ)

HIGH CORRELATION 

Distinct7690
Distinct (%)> 99.9%
Missing22
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean125177.31
Minimum0.58254795
Maximum20851858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-12-13T08:32:28.900923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.58254795
5-th percentile183.82509
Q11437.8129
median8480.069
Q322858.805
95-th percentile298678.05
Maximum20851858
Range20851858
Interquartile range (IQR)21420.992

Descriptive statistics

Standard deviation959506.59
Coefficient of variation (CV)7.6651796
Kurtosis255.37999
Mean125177.31
Median Absolute Deviation (MAD)7503.6215
Skewness15.031586
Sum9.6273872 × 108
Variance9.2065289 × 1011
MonotonicityNot monotonic
2023-12-13T08:32:29.024869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143.426563 2
 
< 0.1%
35163.43955 1
 
< 0.1%
5342.308959 1
 
< 0.1%
7710.899438 1
 
< 0.1%
12348.00034 1
 
< 0.1%
5714.559705 1
 
< 0.1%
2262.82401 1
 
< 0.1%
7337.105333 1
 
< 0.1%
12968.46633 1
 
< 0.1%
39098.48568 1
 
< 0.1%
Other values (7680) 7680
99.6%
(Missing) 22
 
0.3%
ValueCountFrequency (%)
0.58254795 1
< 0.1%
0.582814 1
< 0.1%
0.801091 1
< 0.1%
0.981303 1
< 0.1%
1.64196396 1
< 0.1%
2.83955608 1
< 0.1%
4.682199974 1
< 0.1%
4.723936 1
< 0.1%
5.0363 1
< 0.1%
5.67229 1
< 0.1%
ValueCountFrequency (%)
20851858.35 1
< 0.1%
20478874.47 1
< 0.1%
20418645.22 1
< 0.1%
20375264.03 1
< 0.1%
20315081.9 1
< 0.1%
17921702.95 1
< 0.1%
17690031.36 1
< 0.1%
17589664.85 1
< 0.1%
17227044.82 1
< 0.1%
15959409.95 1
< 0.1%

대기오염물질 총량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3315
Distinct (%)96.3%
Missing4272
Missing (%)55.4%
Infinite0
Infinite (%)0.0%
Mean588.05648
Minimum2.9 × 10-5
Maximum67685.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-12-13T08:32:29.164168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9 × 10-5
5-th percentile0.17338406
Q13.52
median19.318
Q375.294
95-th percentile1168.068
Maximum67685.1
Range67685.1
Interquartile range (IQR)71.774

Descriptive statistics

Standard deviation3643.6712
Coefficient of variation (CV)6.1961245
Kurtosis131.87365
Mean588.05648
Median Absolute Deviation (MAD)18.099996
Skewness10.544226
Sum2023502.4
Variance13276340
MonotonicityNot monotonic
2023-12-13T08:32:29.602977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 5
 
0.1%
26.787 5
 
0.1%
17.27 4
 
0.1%
213.75 4
 
0.1%
19.45 4
 
0.1%
0.36 3
 
< 0.1%
7.1 3
 
< 0.1%
0.114 3
 
< 0.1%
0.8 3
 
< 0.1%
3.97 3
 
< 0.1%
Other values (3305) 3404
44.1%
(Missing) 4272
55.4%
ValueCountFrequency (%)
2.9e-05 1
< 0.1%
3.57601e-05 1
< 0.1%
3.6e-05 1
< 0.1%
3.9812e-05 1
< 0.1%
5.91689e-05 1
< 0.1%
7.66231e-05 1
< 0.1%
0.000132545 1
< 0.1%
0.00014 1
< 0.1%
0.00041 2
< 0.1%
0.000705 1
< 0.1%
ValueCountFrequency (%)
67685.1 1
< 0.1%
55699.243 1
< 0.1%
52507.062 1
< 0.1%
51730.08 1
< 0.1%
51124.19 1
< 0.1%
50239.06 1
< 0.1%
49916.721 1
< 0.1%
42138.071 1
< 0.1%
40651.11 1
< 0.1%
39006.85549 1
< 0.1%

수질오염물질 총량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2938
Distinct (%)98.9%
Missing4741
Missing (%)61.5%
Infinite0
Infinite (%)0.0%
Mean191.09931
Minimum1 × 10-5
Maximum22967.209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-12-13T08:32:29.747107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile0.063070206
Q11.78815
median11.853093
Q366.131835
95-th percentile609.46247
Maximum22967.209
Range22967.209
Interquartile range (IQR)64.343685

Descriptive statistics

Standard deviation1138.5757
Coefficient of variation (CV)5.9580315
Kurtosis213.35585
Mean191.09931
Median Absolute Deviation (MAD)11.617093
Skewness13.596051
Sum567947.16
Variance1296354.7
MonotonicityNot monotonic
2023-12-13T08:32:29.898841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.228 3
 
< 0.1%
0.060798 3
 
< 0.1%
0.619 2
 
< 0.1%
13.598 2
 
< 0.1%
0.0005 2
 
< 0.1%
0.613 2
 
< 0.1%
0.601 2
 
< 0.1%
4.35 2
 
< 0.1%
0.266 2
 
< 0.1%
3.1 2
 
< 0.1%
Other values (2928) 2950
38.2%
(Missing) 4741
61.5%
ValueCountFrequency (%)
1e-05 1
< 0.1%
0.000109656 1
< 0.1%
0.000151829 1
< 0.1%
0.000187 1
< 0.1%
0.00035028 1
< 0.1%
0.0004 1
< 0.1%
0.0005 2
< 0.1%
0.001329771 1
< 0.1%
0.00133 1
< 0.1%
0.0013503 1
< 0.1%
ValueCountFrequency (%)
22967.20916 1
< 0.1%
21726.0 1
< 0.1%
19834.00257 1
< 0.1%
17838.6 1
< 0.1%
17510.9 1
< 0.1%
16699.36726 1
< 0.1%
15562.98045 1
< 0.1%
15405.31 1
< 0.1%
11809.78443 1
< 0.1%
11541.21058 1
< 0.1%

폐기물 재활용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4519
Distinct (%)91.9%
Missing2794
Missing (%)36.2%
Infinite0
Infinite (%)0.0%
Mean43184.752
Minimum0.002
Maximum16288021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-12-13T08:32:30.071109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile2.8513
Q156.905
median688.12
Q35989.002
95-th percentile59104.582
Maximum16288021
Range16288021
Interquartile range (IQR)5932.097

Descriptive statistics

Standard deviation523553.19
Coefficient of variation (CV)12.123566
Kurtosis479.35461
Mean43184.752
Median Absolute Deviation (MAD)683.51
Skewness21.009533
Sum2.124258 × 108
Variance2.7410795 × 1011
MonotonicityNot monotonic
2023-12-13T08:32:30.247899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 18
 
0.2%
2.0 14
 
0.2%
5.0 13
 
0.2%
3.0 10
 
0.1%
6.0 9
 
0.1%
0.6 9
 
0.1%
0.5 8
 
0.1%
1.5 8
 
0.1%
18.0 8
 
0.1%
1.2 8
 
0.1%
Other values (4509) 4814
62.4%
(Missing) 2794
36.2%
ValueCountFrequency (%)
0.002 1
 
< 0.1%
0.009 1
 
< 0.1%
0.01 3
< 0.1%
0.011 1
 
< 0.1%
0.029 1
 
< 0.1%
0.05 3
< 0.1%
0.08 3
< 0.1%
0.082 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 4
0.1%
ValueCountFrequency (%)
16288021.4 1
< 0.1%
11234117.0 1
< 0.1%
10892528.0 1
< 0.1%
10539946.24 1
< 0.1%
10405628.58 1
< 0.1%
10320554.76 1
< 0.1%
10280613.0 1
< 0.1%
10209335.0 1
< 0.1%
10204679.39 1
< 0.1%
10093813.6 1
< 0.1%

폐기물발생 총량
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7198
Distinct (%)93.9%
Missing44
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean34828.947
Minimum0.062115148
Maximum16714591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-12-13T08:32:30.427277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.062115148
5-th percentile8.0493268
Q1110
median812.74
Q36520.9663
95-th percentile67778.554
Maximum16714591
Range16714590
Interquartile range (IQR)6410.9663

Descriptive statistics

Standard deviation432815
Coefficient of variation (CV)12.426876
Kurtosis742.09872
Mean34828.947
Median Absolute Deviation (MAD)796.54
Skewness25.978334
Sum2.6710319 × 108
Variance1.8732883 × 1011
MonotonicityNot monotonic
2023-12-13T08:32:30.577668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 12
 
0.2%
30.0 12
 
0.2%
36.0 11
 
0.1%
7.5 10
 
0.1%
15.0 10
 
0.1%
5.0 10
 
0.1%
27.0 9
 
0.1%
18.0 9
 
0.1%
12.0 9
 
0.1%
8.0 7
 
0.1%
Other values (7188) 7570
98.1%
(Missing) 44
 
0.6%
ValueCountFrequency (%)
0.062115148 1
 
< 0.1%
0.1 3
< 0.1%
0.12 1
 
< 0.1%
0.1824 1
 
< 0.1%
0.19 1
 
< 0.1%
0.225 1
 
< 0.1%
0.26 5
0.1%
0.268 1
 
< 0.1%
0.2682 1
 
< 0.1%
0.275 1
 
< 0.1%
ValueCountFrequency (%)
16714590.55 1
< 0.1%
11744273.2 1
< 0.1%
11296936.91 1
< 0.1%
11166100.57 1
< 0.1%
10756116.06 1
< 0.1%
10670880.28 1
< 0.1%
10589039.85 1
< 0.1%
10493064.18 1
< 0.1%
10295715.11 1
< 0.1%
10227423.44 1
< 0.1%

화학물질 배출량
Real number (ℝ)

MISSING 

Distinct2498
Distinct (%)94.6%
Missing5073
Missing (%)65.8%
Infinite0
Infinite (%)0.0%
Mean10822.286
Minimum5 × 10-5
Maximum2284591.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2023-12-13T08:32:30.754296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5 × 10-5
5-th percentile0.03125
Q11.87675
median24.50465
Q3257.3505
95-th percentile7743.2306
Maximum2284591.1
Range2284591.1
Interquartile range (IQR)255.47375

Descriptive statistics

Standard deviation94556.826
Coefficient of variation (CV)8.737232
Kurtosis261.22335
Mean10822.286
Median Absolute Deviation (MAD)24.4274
Skewness14.630198
Sum28570836
Variance8.9409934 × 109
MonotonicityNot monotonic
2023-12-13T08:32:30.914825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001 6
 
0.1%
0.0001 5
 
0.1%
1.1 4
 
0.1%
0.03 4
 
0.1%
0.002 4
 
0.1%
0.011 3
 
< 0.1%
0.005 3
 
< 0.1%
71.0 3
 
< 0.1%
85.7 3
 
< 0.1%
25.36 3
 
< 0.1%
Other values (2488) 2602
33.7%
(Missing) 5073
65.8%
ValueCountFrequency (%)
5e-05 1
 
< 0.1%
0.0001 5
0.1%
0.0002 2
 
< 0.1%
0.0003 2
 
< 0.1%
0.0004 1
 
< 0.1%
0.0005 2
 
< 0.1%
0.0006 1
 
< 0.1%
0.0008 2
 
< 0.1%
0.001 6
0.1%
0.0011 1
 
< 0.1%
ValueCountFrequency (%)
2284591.114 1
< 0.1%
1916144.0 1
< 0.1%
1493483.09 1
< 0.1%
1379341.9 1
< 0.1%
1211925.74 1
< 0.1%
1178636.0 1
< 0.1%
928234.059 1
< 0.1%
884374.0 1
< 0.1%
845032.024 1
< 0.1%
765911.0 1
< 0.1%

Interactions

2023-12-13T08:32:23.908190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:17.720009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.533881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.257686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.959388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.770757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:21.608928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:22.897668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:24.043109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:17.849180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.627382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.339713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.065335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.866814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:21.730214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:23.031740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:24.151502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:17.966768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.707631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.422308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.152982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.983944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:21.859398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:23.136452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:24.267177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.057403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.791759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.512654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.235184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:21.086560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:22.003103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:23.266206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:24.381443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.155246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.880707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.596492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.390176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:21.184623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:22.104582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:23.398472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:24.492673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.263350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.967378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.680040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.478253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:21.265727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:22.544846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:23.521583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:24.599482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.362639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.063521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.769975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.590458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:21.378492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:22.673944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:23.678122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:24.722790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:18.451802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.152348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:19.864529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:20.688205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:21.506439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:22.779454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:32:23.790137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:32:31.055941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형업종세부업종년도용수 사용량용수 재활용량에너지 총량대기오염물질 총량수질오염물질 총량폐기물 재활용량폐기물발생 총량화학물질 배출량
유형1.0000.9640.9810.0290.0970.0000.1930.2400.3440.1270.1270.000
업종0.9641.0001.0000.0160.0000.0000.2320.2190.3530.2030.2470.000
세부업종0.9811.0001.0000.0000.3290.0000.3250.2750.4120.2230.2360.000
년도0.0290.0160.0001.0000.0000.0000.0000.0000.0000.0000.0000.129
용수 사용량0.0970.0000.3290.0001.0000.8480.7200.8250.7590.8490.8820.410
용수 재활용량0.0000.0000.0000.0000.8481.0000.7650.6150.6620.5900.6660.459
에너지 총량0.1930.2320.3250.0000.7200.7651.0000.8390.0000.8270.8440.439
대기오염물질 총량0.2400.2190.2750.0000.8250.6150.8391.0000.0000.9110.9610.255
수질오염물질 총량0.3440.3530.4120.0000.7590.6620.0000.0001.0000.0000.0000.000
폐기물 재활용량0.1270.2030.2230.0000.8490.5900.8270.9110.0001.0000.9810.212
폐기물발생 총량0.1270.2470.2360.0000.8820.6660.8440.9610.0000.9811.0000.271
화학물질 배출량0.0000.0000.0000.1290.4100.4590.4390.2550.0000.2120.2711.000
2023-12-13T08:32:31.214228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도업종유형
년도1.0000.0070.020
업종0.0071.0000.863
유형0.0200.8631.000
2023-12-13T08:32:31.319825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용수 사용량용수 재활용량에너지 총량대기오염물질 총량수질오염물질 총량폐기물 재활용량폐기물발생 총량화학물질 배출량유형업종년도
용수 사용량1.0000.7610.7580.4000.6250.6220.7400.2290.0510.0000.000
용수 재활용량0.7611.0000.6830.3690.4450.5820.5400.1770.0000.0000.000
에너지 총량0.7580.6831.0000.5630.3580.6370.6500.1630.0970.0940.000
대기오염물질 총량0.4000.3690.5631.0000.2770.3830.3740.0870.1840.0690.000
수질오염물질 총량0.6250.4450.3580.2771.0000.4900.5260.2300.1880.1460.000
폐기물 재활용량0.6220.5820.6370.3830.4901.0000.8620.2080.0860.1050.000
폐기물발생 총량0.7400.5400.6500.3740.5260.8621.0000.2380.0860.1080.000
화학물질 배출량0.2290.1770.1630.0870.2300.2080.2381.0000.0000.0000.075
유형0.0510.0000.0970.1840.1880.0860.0860.0001.0000.8630.020
업종0.0000.0000.0940.0690.1460.1050.1080.0000.8631.0000.007
년도0.0000.0000.0000.0000.0000.0000.0000.0750.0200.0071.000

Missing values

2023-12-13T08:32:24.876845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:32:25.127976image/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.
2023-12-13T08:32:25.320965image/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

대표사업장코드사업장코드대표사업장명유형업종세부업종년도용수 사용량용수 재활용량에너지 총량대기오염물질 총량수질오염물질 총량폐기물 재활용량폐기물발생 총량화학물질 배출량
00000000000000009529300000000000000095293LS산전(주) 천안사업장제조제조업전기장비 제조업201565665.0665.24563.0392621.027180.005145477.06716.4216.639
10000000000000009529600000000000000095296한국지엠(주) 부평공장 (본사)제조제조업자동차 및 트레일러 제조업20152647353.0106292.0163515.2112220.72255.148213797.5221813.7731457.03
20000000000000009529700000000000000095297ROHMKOREA 대전공장제조제조업전자부품, 컴퓨터, 영상, 음향 및 통신장비 제조업2015177540.071564.08671.6896390.8141.479462.1619.88432.62
30000000000000009529800000000000000095298(주)만도 brake 평택사업본부제조제조업자동차 및 트레일러 제조업2015720849.0<NA>59061.7903846.2341821.411225335.72530759.355317.765
40000000000000009529900000000000000095299에스케이하이이엔지㈜기타서비스사업시설관리 및 사업지원 서비스업사업지원 서비스업201533306.6<NA>6496.246084<NA>545.6244613.046767.1130728.0
50000000000000009530000000000000000095300롯데정밀화학(주)제조제조업화학물질 및 화학제품 제조업;의약품 제외201511586158.0208990.0327996.787593.614594.61624504.5170367.031379341.9
60000000000000009530300000000000000095303(주)한국야쿠르트 논산공장제조제조업음료 제조업2015164518.0131376.02538.2339840.7978223.625461.84822.37171.944
70000000000000009530500000000000000095305LS일렉트릭 청주사업장제조제조업전기장비 제조업201596210.0<NA>9440.4064412.739<NA>2779.693472.420.692
80000000000000009530700000000000000095307서울우유협동조합 거창공장제조제조업음료 제조업2015460462.037367.06665.008673<NA>3.4222122778.653132.02519.894
90000000000000009531100000000000000095311(주)휴비스 전주공장제조제조업화학물질 및 화학제품 제조업;의약품 제외20153271221.0922310.0174285.2853510.8365.85612939.73215160.419588.802
대표사업장코드사업장코드대표사업장명유형업종세부업종년도용수 사용량용수 재활용량에너지 총량대기오염물질 총량수질오염물질 총량폐기물 재활용량폐기물발생 총량화학물질 배출량
7703<NA><NA>동해시시설관리공단기타서비스사업시설관리 및 사업지원 서비스업사업지원 서비스업201918863.0<NA>267.447808<NA><NA><NA>96.7375<NA>
7704<NA><NA>(주)전주원파워제조제조업기타 제품 제조업2019207723.0<NA>179406.9307276.58165.47515434.123159.2<NA>
7705<NA><NA>람정제주개발 주식회사기타서비스부동산업 및 임대업부동산업2019721796.0249848.015763.48607<NA><NA>2465.625702.02<NA>
7706<NA><NA>(주)명신제조제조업자동차 및 트레일러 제조업20192478.0<NA>4263.150124<NA><NA><NA>33.74<NA>
7707<NA><NA>bat korea 제조(주)제조제조업담배 제조업2019112644.0<NA>10893.714627.053.772338.515044.27<NA>
7708<NA><NA>한국제이씨씨(주)제조제조업전자부품, 컴퓨터, 영상, 음향 및 통신장비 제조업2019149147.0<NA>5406.9943280.2593.716<NA>1657.641.169
7709<NA><NA>파인트리포스마그네슘(주)제조제조업1차 금속 제조업20193065.7<NA>1722.647048<NA><NA><NA>27.76<NA>
7710<NA><NA>포스코인터내셔널 송도 본사제조도매 및 소매업도매 및 상품중개업2019146652.0<NA>5624.585894<NA><NA>89.0589.05<NA>
7711<NA><NA>(주)토탈기타산업하수·폐기물 처리, 원료재생 및 환경복원업폐기물 수집운반, 처리 및 원료재생업201933294.0<NA>932.10272131.779<NA><NA>13567.512.3287
7712<NA><NA>지엠테크니컬센터코리아제조전문, 과학 및 기술 서비스업연구개발업20192121.76<NA>9314.3004980.531.3992922.2557.01<NA>