Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells20413
Missing cells (%)12.8%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.4 MiB
Average record size in memory145.0 B

Variable types

Categorical4
Text3
Numeric9

Dataset

Description국가 대기오염물질 배출량 정보를 제공한다. 해당 배출량 통계는 2021년 1월부터 12월까지 전국에서 발생한 배출량에 대한 통계자료로, 기초자료 수집 및 검증에 약 2년이 소요된다.
Author환경부 국가미세먼지정보센터
URLhttps://www.data.go.kr/data/15063218/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
연료소분류 is highly overall correlated with 휘발성유기화합물(VOC) and 3 other fieldsHigh correlation
연료대분류 is highly overall correlated with 암모니아(NH3) and 2 other fieldsHigh correlation
일산화탄소(CO) is highly overall correlated with 질소산화물(NOx) and 7 other fieldsHigh correlation
질소산화물(NOx) is highly overall correlated with 일산화탄소(CO) and 7 other fieldsHigh correlation
황산화물(SOx) is highly overall correlated with 일산화탄소(CO) and 7 other fieldsHigh correlation
총 부유입자(TSP) is highly overall correlated with 일산화탄소(CO) and 7 other fieldsHigh correlation
미세먼지(PM-10) is highly overall correlated with 일산화탄소(CO) and 7 other fieldsHigh correlation
초미세먼지(PM-2_5) is highly overall correlated with 일산화탄소(CO) and 7 other fieldsHigh correlation
휘발성유기화합물(VOC) is highly overall correlated with 일산화탄소(CO) and 8 other fieldsHigh correlation
암모니아(NH3) is highly overall correlated with 일산화탄소(CO) and 8 other fieldsHigh correlation
블랙카본(BC) is highly overall correlated with 일산화탄소(CO) and 7 other fieldsHigh correlation
배출원대분류 is highly overall correlated with 연료대분류High correlation
연료소분류 is highly imbalanced (67.9%)Imbalance
일산화탄소(CO) has 2620 (26.2%) missing valuesMissing
질소산화물(NOx) has 2600 (26.0%) missing valuesMissing
황산화물(SOx) has 3466 (34.7%) missing valuesMissing
총 부유입자(TSP) has 1426 (14.3%) missing valuesMissing
미세먼지(PM-10) has 1438 (14.4%) missing valuesMissing
초미세먼지(PM-2_5) has 1440 (14.4%) missing valuesMissing
휘발성유기화합물(VOC) has 1866 (18.7%) missing valuesMissing
암모니아(NH3) has 2547 (25.5%) missing valuesMissing
블랙카본(BC) has 2995 (29.9%) missing valuesMissing
일산화탄소(CO) is highly skewed (γ1 = 31.85993026)Skewed
질소산화물(NOx) is highly skewed (γ1 = 80.93219639)Skewed
황산화물(SOx) is highly skewed (γ1 = 60.21540691)Skewed
미세먼지(PM-10) is highly skewed (γ1 = 21.11478278)Skewed
초미세먼지(PM-2_5) is highly skewed (γ1 = 56.01453249)Skewed
휘발성유기화합물(VOC) is highly skewed (γ1 = 30.70698174)Skewed
암모니아(NH3) is highly skewed (γ1 = 33.68877858)Skewed
블랙카본(BC) is highly skewed (γ1 = 64.09151453)Skewed
일산화탄소(CO) has 204 (2.0%) zerosZeros
질소산화물(NOx) has 332 (3.3%) zerosZeros
황산화물(SOx) has 1908 (19.1%) zerosZeros
총 부유입자(TSP) has 1682 (16.8%) zerosZeros
미세먼지(PM-10) has 1700 (17.0%) zerosZeros
초미세먼지(PM-2_5) has 1800 (18.0%) zerosZeros
휘발성유기화합물(VOC) has 814 (8.1%) zerosZeros
암모니아(NH3) has 1344 (13.4%) zerosZeros
블랙카본(BC) has 2021 (20.2%) zerosZeros

Reproduction

Analysis started2024-03-23 04:47:08.485506
Analysis finished2024-03-23 04:47:47.663832
Duration39.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
1771 
경상북도
1017 
경상남도
936 
전라남도
871 
서울특별시
786 
Other values (14)
4619 

Length

Max length7
Median length5
Mean length4.0523
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울산광역시
2nd row경기도
3rd row경기도
4th row전라북도
5th row경상남도

Common Values

ValueCountFrequency (%)
경기도 1771
17.7%
경상북도 1017
10.2%
경상남도 936
9.4%
전라남도 871
8.7%
서울특별시 786
7.9%
충청남도 743
7.4%
전라북도 672
 
6.7%
강원도 660
 
6.6%
충청북도 594
 
5.9%
부산광역시 587
 
5.9%
Other values (9) 1363
13.6%

Length

2024-03-23T04:47:47.866816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1771
17.7%
경상북도 1017
10.2%
경상남도 936
9.4%
전라남도 871
8.7%
서울특별시 786
7.9%
충청남도 743
7.4%
전라북도 672
 
6.7%
강원도 660
 
6.6%
충청북도 594
 
5.9%
부산광역시 587
 
5.9%
Other values (9) 1363
13.6%
Distinct229
Distinct (%)2.3%
Missing5
Missing (%)< 0.1%
Memory size156.2 KiB
2024-03-23T04:47:48.741221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.5313657
Min length2

Characters and Unicode

Total characters35296
Distinct characters145
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row남구
2nd row과천시
3rd row고양시 일산동구
4th row김제시
5th row거제시
ValueCountFrequency (%)
창원시 226
 
2.0%
중구 215
 
1.9%
남구 207
 
1.8%
동구 204
 
1.8%
청주시 189
 
1.7%
북구 183
 
1.6%
서구 181
 
1.6%
수원시 157
 
1.4%
용인시 151
 
1.3%
고양시 131
 
1.1%
Other values (228) 9570
83.8%
2024-03-23T04:47:50.535543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4463
 
12.6%
3975
 
11.3%
3301
 
9.4%
1419
 
4.0%
1109
 
3.1%
1027
 
2.9%
933
 
2.6%
915
 
2.6%
845
 
2.4%
764
 
2.2%
Other values (135) 16545
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33877
96.0%
Space Separator 1419
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4463
 
13.2%
3975
 
11.7%
3301
 
9.7%
1109
 
3.3%
1027
 
3.0%
933
 
2.8%
915
 
2.7%
845
 
2.5%
764
 
2.3%
764
 
2.3%
Other values (134) 15781
46.6%
Space Separator
ValueCountFrequency (%)
1419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33877
96.0%
Common 1419
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4463
 
13.2%
3975
 
11.7%
3301
 
9.7%
1109
 
3.3%
1027
 
3.0%
933
 
2.8%
915
 
2.7%
845
 
2.5%
764
 
2.3%
764
 
2.3%
Other values (134) 15781
46.6%
Common
ValueCountFrequency (%)
1419
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33877
96.0%
ASCII 1419
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4463
 
13.2%
3975
 
11.7%
3301
 
9.7%
1109
 
3.3%
1027
 
3.0%
933
 
2.8%
915
 
2.7%
845
 
2.5%
764
 
2.3%
764
 
2.3%
Other values (134) 15781
46.6%
ASCII
ValueCountFrequency (%)
1419
100.0%

배출원대분류
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
도로이동오염원
2921 
제조업 연소
1715 
비산먼지
1175 
비도로이동오염원
917 
생물성 연소
820 
Other values (9)
2452 

Length

Max length10
Median length8
Mean length6.132
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타 면오염원
2nd row비산먼지
3rd row제조업 연소
4th row제조업 연소
5th row비도로이동오염원

Common Values

ValueCountFrequency (%)
도로이동오염원 2921
29.2%
제조업 연소 1715
17.2%
비산먼지 1175
11.8%
비도로이동오염원 917
 
9.2%
생물성 연소 820
 
8.2%
비산업 연소 806
 
8.1%
유기용제 사용 606
 
6.1%
농업 427
 
4.3%
폐기물처리 212
 
2.1%
기타 면오염원 139
 
1.4%
Other values (4) 262
 
2.6%

Length

2024-03-23T04:47:51.355038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연소 3397
23.8%
도로이동오염원 2921
20.5%
제조업 1715
12.0%
비산먼지 1175
 
8.2%
비도로이동오염원 917
 
6.4%
생물성 820
 
5.7%
비산업 806
 
5.6%
유기용제 606
 
4.2%
사용 606
 
4.2%
농업 427
 
3.0%
Other values (9) 882
 
6.2%
Distinct56
Distinct (%)0.6%
Missing5
Missing (%)< 0.1%
Memory size156.2 KiB
2024-03-23T04:47:52.064782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.4525263
Min length2

Characters and Unicode

Total characters44503
Distinct characters107
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
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 row동물
2nd row도로재비산먼지
3rd row기타
4th row기타
5th row농업기계
ValueCountFrequency (%)
기타 1761
 
13.9%
승용차 752
 
5.9%
694
 
5.5%
화물차 604
 
4.8%
건설장비 466
 
3.7%
rv 454
 
3.6%
승합차 412
 
3.3%
소각 387
 
3.1%
분뇨관리 382
 
3.0%
상업 377
 
3.0%
Other values (57) 6356
50.3%
2024-03-23T04:47:52.960729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3077
 
6.9%
2650
 
6.0%
2137
 
4.8%
1951
 
4.4%
1761
 
4.0%
1490
 
3.3%
1463
 
3.3%
1456
 
3.3%
1389
 
3.1%
1249
 
2.8%
Other values (97) 25880
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40662
91.4%
Space Separator 2650
 
6.0%
Uppercase Letter 908
 
2.0%
Other Punctuation 283
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3077
 
7.6%
2137
 
5.3%
1951
 
4.8%
1761
 
4.3%
1490
 
3.7%
1463
 
3.6%
1456
 
3.6%
1389
 
3.4%
1249
 
3.1%
1173
 
2.9%
Other values (92) 23516
57.8%
Uppercase Letter
ValueCountFrequency (%)
R 454
50.0%
V 454
50.0%
Other Punctuation
ValueCountFrequency (%)
. 258
91.2%
, 25
 
8.8%
Space Separator
ValueCountFrequency (%)
2650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40662
91.4%
Common 2933
 
6.6%
Latin 908
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3077
 
7.6%
2137
 
5.3%
1951
 
4.8%
1761
 
4.3%
1490
 
3.7%
1463
 
3.6%
1456
 
3.6%
1389
 
3.4%
1249
 
3.1%
1173
 
2.9%
Other values (92) 23516
57.8%
Common
ValueCountFrequency (%)
2650
90.4%
. 258
 
8.8%
, 25
 
0.9%
Latin
ValueCountFrequency (%)
R 454
50.0%
V 454
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40662
91.4%
ASCII 3841
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3077
 
7.6%
2137
 
5.3%
1951
 
4.8%
1761
 
4.3%
1490
 
3.7%
1463
 
3.6%
1456
 
3.6%
1389
 
3.4%
1249
 
3.1%
1173
 
2.9%
Other values (92) 23516
57.8%
ASCII
ValueCountFrequency (%)
2650
69.0%
R 454
 
11.8%
V 454
 
11.8%
. 258
 
6.7%
, 25
 
0.7%
Distinct181
Distinct (%)1.8%
Missing5
Missing (%)< 0.1%
Memory size156.2 KiB
2024-03-23T04:47:53.463376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length5.405903
Min length1

Characters and Unicode

Total characters54032
Distinct characters253
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

Unique21 ?
Unique (%)0.2%

Sample

1st row인간
2nd row승합차
3rd row출판, 인쇄 및 기록매체 복제업
4th row목재 및 나무제품 제조업(가구 제외)
5th row경운기
ValueCountFrequency (%)
기타 1214
 
7.6%
1202
 
7.5%
제조업 1095
 
6.9%
소형 731
 
4.6%
중형 636
 
4.0%
대형 407
 
2.6%
가구 317
 
2.0%
경형 303
 
1.9%
제외 251
 
1.6%
기타제품 235
 
1.5%
Other values (231) 9563
59.9%
2024-03-23T04:47:54.468635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5959
 
11.0%
2975
 
5.5%
2835
 
5.2%
2077
 
3.8%
1921
 
3.6%
1500
 
2.8%
1455
 
2.7%
1275
 
2.4%
1120
 
2.1%
1069
 
2.0%
Other values (243) 31846
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44377
82.1%
Space Separator 5959
 
11.0%
Decimal Number 1041
 
1.9%
Other Punctuation 891
 
1.6%
Open Punctuation 539
 
1.0%
Close Punctuation 539
 
1.0%
Lowercase Letter 416
 
0.8%
Uppercase Letter 269
 
0.5%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2975
 
6.7%
2835
 
6.4%
2077
 
4.7%
1921
 
4.3%
1500
 
3.4%
1455
 
3.3%
1275
 
2.9%
1120
 
2.5%
1069
 
2.4%
748
 
1.7%
Other values (220) 27402
61.7%
Uppercase Letter
ValueCountFrequency (%)
R 112
41.6%
V 85
31.6%
S 29
 
10.8%
C 28
 
10.4%
N 7
 
2.6%
B 2
 
0.7%
P 2
 
0.7%
D 1
 
0.4%
H 1
 
0.4%
A 1
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 263
25.3%
1 247
23.7%
2 226
21.7%
3 152
14.6%
5 100
 
9.6%
6 53
 
5.1%
Space Separator
ValueCountFrequency (%)
5959
100.0%
Other Punctuation
ValueCountFrequency (%)
, 891
100.0%
Open Punctuation
ValueCountFrequency (%)
( 539
100.0%
Close Punctuation
ValueCountFrequency (%)
) 539
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 416
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44377
82.1%
Common 8970
 
16.6%
Latin 685
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2975
 
6.7%
2835
 
6.4%
2077
 
4.7%
1921
 
4.3%
1500
 
3.4%
1455
 
3.3%
1275
 
2.9%
1120
 
2.5%
1069
 
2.4%
748
 
1.7%
Other values (220) 27402
61.7%
Latin
ValueCountFrequency (%)
c 416
60.7%
R 112
 
16.4%
V 85
 
12.4%
S 29
 
4.2%
C 28
 
4.1%
N 7
 
1.0%
B 2
 
0.3%
P 2
 
0.3%
D 1
 
0.1%
H 1
 
0.1%
Other values (2) 2
 
0.3%
Common
ValueCountFrequency (%)
5959
66.4%
, 891
 
9.9%
( 539
 
6.0%
) 539
 
6.0%
0 263
 
2.9%
1 247
 
2.8%
2 226
 
2.5%
3 152
 
1.7%
5 100
 
1.1%
6 53
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44377
82.1%
ASCII 9655
 
17.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5959
61.7%
, 891
 
9.2%
( 539
 
5.6%
) 539
 
5.6%
c 416
 
4.3%
0 263
 
2.7%
1 247
 
2.6%
2 226
 
2.3%
3 152
 
1.6%
R 112
 
1.2%
Other values (13) 311
 
3.2%
Hangul
ValueCountFrequency (%)
2975
 
6.7%
2835
 
6.4%
2077
 
4.7%
1921
 
4.3%
1500
 
3.4%
1455
 
3.3%
1275
 
2.9%
1120
 
2.5%
1069
 
2.4%
748
 
1.7%
Other values (220) 27402
61.7%

연료대분류
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3585 
경유
2418 
LNG
1122 
LPG
1040 
휘발유
729 
Other values (12)
1106 

Length

Max length5
Median length4
Mean length3.1671
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3585
35.9%
경유 2418
24.2%
LNG 1122
 
11.2%
LPG 1040
 
10.4%
휘발유 729
 
7.3%
등유 356
 
3.6%
하이브리드 323
 
3.2%
B-C유 103
 
1.0%
CNG 95
 
0.9%
B-A유 67
 
0.7%
Other values (7) 162
 
1.6%

Length

2024-03-23T04:47:54.889261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3585
35.8%
경유 2418
24.2%
lng 1122
 
11.2%
lpg 1040
 
10.4%
휘발유 729
 
7.3%
등유 356
 
3.6%
하이브리드 323
 
3.2%
b-c유 103
 
1.0%
cng 95
 
0.9%
b-a유 67
 
0.7%
Other values (8) 170
 
1.7%

연료소분류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7423 
LNG
1122 
실내등유
 
339
경유(0.001%)
 
335
프로판
 
284
Other values (23)
 
497

Length

Max length10
Median length4
Mean length4.1897
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7423
74.2%
LNG 1122
 
11.2%
실내등유 339
 
3.4%
경유(0.001%) 335
 
3.4%
프로판 284
 
2.8%
부탄 138
 
1.4%
B-C유(0.3%) 59
 
0.6%
민수용무연탄 50
 
0.5%
B-C유(0.5%) 38
 
0.4%
B-A유(0.3%) 37
 
0.4%
Other values (18) 175
 
1.8%

Length

2024-03-23T04:47:55.655472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7423
74.2%
lng 1122
 
11.2%
실내등유 339
 
3.4%
경유(0.001 335
 
3.4%
프로판 284
 
2.8%
부탄 138
 
1.4%
b-c유(0.3 59
 
0.6%
민수용무연탄 50
 
0.5%
b-c유(0.5 38
 
0.4%
b-a유(0.3 37
 
0.4%
Other values (18) 175
 
1.8%

일산화탄소(CO)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct3919
Distinct (%)53.1%
Missing2620
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean20349.09
Minimum0
Maximum9154127
Zeros204
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T04:47:56.207541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q153
median771
Q35899.75
95-th percentile66303.75
Maximum9154127
Range9154127
Interquartile range (IQR)5846.75

Descriptive statistics

Standard deviation170466.51
Coefficient of variation (CV)8.3771072
Kurtosis1373.3453
Mean20349.09
Median Absolute Deviation (MAD)768
Skewness31.85993
Sum1.5017628 × 108
Variance2.905883 × 1010
MonotonicityNot monotonic
2024-03-23T04:47:56.836880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 228
 
2.3%
0 204
 
2.0%
2 145
 
1.5%
3 120
 
1.2%
4 99
 
1.0%
5 69
 
0.7%
6 64
 
0.6%
9 46
 
0.5%
7 45
 
0.4%
12 45
 
0.4%
Other values (3909) 6315
63.1%
(Missing) 2620
26.2%
ValueCountFrequency (%)
0 204
2.0%
1 228
2.3%
2 145
1.5%
3 120
1.2%
4 99
1.0%
5 69
 
0.7%
6 64
 
0.6%
7 45
 
0.4%
8 42
 
0.4%
9 46
 
0.5%
ValueCountFrequency (%)
9154127 1
 
< 0.1%
5115094 1
 
< 0.1%
4767984 1
 
< 0.1%
3661651 1
 
< 0.1%
3008178 1
 
< 0.1%
2727615 1
 
< 0.1%
2307407 1
 
< 0.1%
2038945 1
 
< 0.1%
1948565 1
 
< 0.1%
1830825 4
< 0.1%

질소산화물(NOx)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct3925
Distinct (%)53.0%
Missing2600
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean28934.639
Minimum0
Maximum54261568
Zeros332
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T04:47:57.584296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q148
median784.5
Q36697.5
95-th percentile93929.05
Maximum54261568
Range54261568
Interquartile range (IQR)6649.5

Descriptive statistics

Standard deviation644056.88
Coefficient of variation (CV)22.259026
Kurtosis6798.8936
Mean28934.639
Median Absolute Deviation (MAD)782.5
Skewness80.932196
Sum2.1411633 × 108
Variance4.1480926 × 1011
MonotonicityNot monotonic
2024-03-23T04:47:58.181480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 332
 
3.3%
1 202
 
2.0%
2 105
 
1.1%
3 85
 
0.9%
4 76
 
0.8%
7 62
 
0.6%
8 47
 
0.5%
11 47
 
0.5%
9 45
 
0.4%
10 44
 
0.4%
Other values (3915) 6355
63.5%
(Missing) 2600
26.0%
ValueCountFrequency (%)
0 332
3.3%
1 202
2.0%
2 105
 
1.1%
3 85
 
0.9%
4 76
 
0.8%
5 33
 
0.3%
6 43
 
0.4%
7 62
 
0.6%
8 47
 
0.5%
9 45
 
0.4%
ValueCountFrequency (%)
54261568 1
< 0.1%
6245805 1
< 0.1%
4379375 1
< 0.1%
2808729 1
< 0.1%
2733949 1
< 0.1%
2561919 1
< 0.1%
2251131 1
< 0.1%
2245563 1
< 0.1%
1993726 1
< 0.1%
1752526 1
< 0.1%

황산화물(SOx)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct634
Distinct (%)9.7%
Missing3466
Missing (%)34.7%
Infinite0
Infinite (%)0.0%
Mean6902.8272
Minimum0
Maximum16617992
Zeros1908
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T04:47:58.810951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile594.4
Maximum16617992
Range16617992
Interquartile range (IQR)22

Descriptive statistics

Standard deviation234109.04
Coefficient of variation (CV)33.91495
Kurtosis4040.0928
Mean6902.8272
Median Absolute Deviation (MAD)3
Skewness60.215407
Sum45103073
Variance5.4807042 × 1010
MonotonicityNot monotonic
2024-03-23T04:47:59.474845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1908
19.1%
1 766
 
7.7%
2 412
 
4.1%
3 280
 
2.8%
4 205
 
2.1%
5 169
 
1.7%
6 146
 
1.5%
8 135
 
1.4%
10 108
 
1.1%
7 106
 
1.1%
Other values (624) 2299
23.0%
(Missing) 3466
34.7%
ValueCountFrequency (%)
0 1908
19.1%
1 766
7.7%
2 412
 
4.1%
3 280
 
2.8%
4 205
 
2.1%
5 169
 
1.7%
6 146
 
1.5%
7 106
 
1.1%
8 135
 
1.4%
9 101
 
1.0%
ValueCountFrequency (%)
16617992 1
< 0.1%
7397184 1
< 0.1%
3209619 1
< 0.1%
1955859 1
< 0.1%
1829620 1
< 0.1%
1761587 1
< 0.1%
1547730 1
< 0.1%
1123070 1
< 0.1%
1052412 1
< 0.1%
806663 1
< 0.1%

총 부유입자(TSP)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2520
Distinct (%)29.4%
Missing1426
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean10223.77
Minimum0
Maximum1891424
Zeros1682
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T04:48:00.068345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median38
Q3703
95-th percentile40724.7
Maximum1891424
Range1891424
Interquartile range (IQR)702

Descriptive statistics

Standard deviation58130.827
Coefficient of variation (CV)5.6858503
Kurtosis249.50346
Mean10223.77
Median Absolute Deviation (MAD)38
Skewness12.775916
Sum87658605
Variance3.379193 × 109
MonotonicityNot monotonic
2024-03-23T04:48:00.921405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1682
 
16.8%
1 492
 
4.9%
2 231
 
2.3%
3 186
 
1.9%
4 144
 
1.4%
5 139
 
1.4%
6 117
 
1.2%
12 87
 
0.9%
7 86
 
0.9%
8 85
 
0.9%
Other values (2510) 5325
53.2%
(Missing) 1426
 
14.3%
ValueCountFrequency (%)
0 1682
16.8%
1 492
 
4.9%
2 231
 
2.3%
3 186
 
1.9%
4 144
 
1.4%
5 139
 
1.4%
6 117
 
1.2%
7 86
 
0.9%
8 85
 
0.9%
9 70
 
0.7%
ValueCountFrequency (%)
1891424 1
< 0.1%
1369741 1
< 0.1%
1204925 1
< 0.1%
1036850 1
< 0.1%
936580 1
< 0.1%
904156 1
< 0.1%
778533 1
< 0.1%
757837 1
< 0.1%
756188 1
< 0.1%
746946 1
< 0.1%

미세먼지(PM-10)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct2321
Distinct (%)27.1%
Missing1438
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean3568.156
Minimum0
Maximum1036850
Zeros1700
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T04:48:02.074950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median34
Q3522
95-th percentile14538.2
Maximum1036850
Range1036850
Interquartile range (IQR)521

Descriptive statistics

Standard deviation21406.216
Coefficient of variation (CV)5.9992377
Kurtosis756.42234
Mean3568.156
Median Absolute Deviation (MAD)34
Skewness21.114783
Sum30550552
Variance4.5822609 × 108
MonotonicityNot monotonic
2024-03-23T04:48:03.085093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1700
 
17.0%
1 492
 
4.9%
2 250
 
2.5%
3 190
 
1.9%
4 162
 
1.6%
5 137
 
1.4%
6 104
 
1.0%
7 98
 
1.0%
12 84
 
0.8%
8 82
 
0.8%
Other values (2311) 5263
52.6%
(Missing) 1438
 
14.4%
ValueCountFrequency (%)
0 1700
17.0%
1 492
 
4.9%
2 250
 
2.5%
3 190
 
1.9%
4 162
 
1.6%
5 137
 
1.4%
6 104
 
1.0%
7 98
 
1.0%
8 82
 
0.8%
9 74
 
0.7%
ValueCountFrequency (%)
1036850 1
< 0.1%
521549 1
< 0.1%
441677 1
< 0.1%
391417 1
< 0.1%
365972 1
< 0.1%
326670 1
< 0.1%
324074 1
< 0.1%
323095 1
< 0.1%
262923 1
< 0.1%
255211 1
< 0.1%

초미세먼지(PM-2_5)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1995
Distinct (%)23.3%
Missing1440
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean1386.9779
Minimum0
Maximum967711
Zeros1800
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T04:48:04.108529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median26
Q3347
95-th percentile5127.35
Maximum967711
Range967711
Interquartile range (IQR)346

Descriptive statistics

Standard deviation12560.228
Coefficient of variation (CV)9.0558238
Kurtosis4128.7684
Mean1386.9779
Median Absolute Deviation (MAD)26
Skewness56.014532
Sum11872531
Variance1.5775932 × 108
MonotonicityNot monotonic
2024-03-23T04:48:04.944235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1800
 
18.0%
1 548
 
5.5%
2 259
 
2.6%
3 218
 
2.2%
4 141
 
1.4%
5 140
 
1.4%
6 123
 
1.2%
11 92
 
0.9%
7 86
 
0.9%
8 82
 
0.8%
Other values (1985) 5071
50.7%
(Missing) 1440
 
14.4%
ValueCountFrequency (%)
0 1800
18.0%
1 548
 
5.5%
2 259
 
2.6%
3 218
 
2.2%
4 141
 
1.4%
5 140
 
1.4%
6 123
 
1.2%
7 86
 
0.9%
8 82
 
0.8%
9 63
 
0.6%
ValueCountFrequency (%)
967711 1
< 0.1%
239796 1
< 0.1%
190052 1
< 0.1%
147918 1
< 0.1%
146447 1
< 0.1%
142548 1
< 0.1%
133578 1
< 0.1%
129411 1
< 0.1%
127728 1
< 0.1%
127536 1
< 0.1%

휘발성유기화합물(VOC)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct3093
Distinct (%)38.0%
Missing1866
Missing (%)18.7%
Infinite0
Infinite (%)0.0%
Mean25081.083
Minimum0
Maximum12269826
Zeros814
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T04:48:05.565184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median142
Q31964
95-th percentile54934.9
Maximum12269826
Range12269826
Interquartile range (IQR)1957

Descriptive statistics

Standard deviation239200.48
Coefficient of variation (CV)9.5370874
Kurtosis1287.0079
Mean25081.083
Median Absolute Deviation (MAD)142
Skewness30.706982
Sum2.0400953 × 108
Variance5.7216869 × 1010
MonotonicityNot monotonic
2024-03-23T04:48:06.110653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 814
 
8.1%
1 414
 
4.1%
2 224
 
2.2%
3 173
 
1.7%
4 129
 
1.3%
5 124
 
1.2%
6 98
 
1.0%
7 79
 
0.8%
8 78
 
0.8%
10 73
 
0.7%
Other values (3083) 5928
59.3%
(Missing) 1866
 
18.7%
ValueCountFrequency (%)
0 814
8.1%
1 414
4.1%
2 224
 
2.2%
3 173
 
1.7%
4 129
 
1.3%
5 124
 
1.2%
6 98
 
1.0%
7 79
 
0.8%
8 78
 
0.8%
9 63
 
0.6%
ValueCountFrequency (%)
12269826 1
< 0.1%
9810841 1
< 0.1%
5841129 1
< 0.1%
4917450 1
< 0.1%
4434328 1
< 0.1%
3694996 1
< 0.1%
3074915 1
< 0.1%
3019385 1
< 0.1%
2414952 1
< 0.1%
2307629 1
< 0.1%

암모니아(NH3)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1075
Distinct (%)14.4%
Missing2547
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean8498.4012
Minimum0
Maximum5828344
Zeros1344
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T04:48:06.723014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q377
95-th percentile2445.4
Maximum5828344
Range5828344
Interquartile range (IQR)76

Descriptive statistics

Standard deviation116444.27
Coefficient of variation (CV)13.701903
Kurtosis1442.2835
Mean8498.4012
Median Absolute Deviation (MAD)11
Skewness33.688779
Sum63338584
Variance1.3559268 × 1010
MonotonicityNot monotonic
2024-03-23T04:48:07.291603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1344
 
13.4%
1 657
 
6.6%
2 372
 
3.7%
3 312
 
3.1%
4 206
 
2.1%
5 194
 
1.9%
6 154
 
1.5%
7 110
 
1.1%
8 107
 
1.1%
9 103
 
1.0%
Other values (1065) 3894
38.9%
(Missing) 2547
25.5%
ValueCountFrequency (%)
0 1344
13.4%
1 657
6.6%
2 372
 
3.7%
3 312
 
3.1%
4 206
 
2.1%
5 194
 
1.9%
6 154
 
1.5%
7 110
 
1.1%
8 107
 
1.1%
9 103
 
1.0%
ValueCountFrequency (%)
5828344 1
< 0.1%
5095465 1
< 0.1%
3314143 1
< 0.1%
2075664 1
< 0.1%
1624925 1
< 0.1%
1292161 1
< 0.1%
1220804 1
< 0.1%
1188373 1
< 0.1%
1148165 1
< 0.1%
1096548 1
< 0.1%

블랙카본(BC)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1081
Distinct (%)15.4%
Missing2995
Missing (%)29.9%
Infinite0
Infinite (%)0.0%
Mean361.08423
Minimum0
Maximum299991
Zeros2021
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T04:48:07.805120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q379
95-th percentile1399.4
Maximum299991
Range299991
Interquartile range (IQR)79

Descriptive statistics

Standard deviation3935.177
Coefficient of variation (CV)10.898225
Kurtosis4808.987
Mean361.08423
Median Absolute Deviation (MAD)6
Skewness64.091515
Sum2529395
Variance15485618
MonotonicityNot monotonic
2024-03-23T04:48:08.479068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2021
20.2%
1 621
 
6.2%
2 300
 
3.0%
3 209
 
2.1%
4 175
 
1.8%
5 170
 
1.7%
6 113
 
1.1%
7 105
 
1.1%
8 80
 
0.8%
9 79
 
0.8%
Other values (1071) 3132
31.3%
(Missing) 2995
29.9%
ValueCountFrequency (%)
0 2021
20.2%
1 621
 
6.2%
2 300
 
3.0%
3 209
 
2.1%
4 175
 
1.8%
5 170
 
1.7%
6 113
 
1.1%
7 105
 
1.1%
8 80
 
0.8%
9 79
 
0.8%
ValueCountFrequency (%)
299991 1
< 0.1%
49495 1
< 0.1%
41390 1
< 0.1%
35909 1
< 0.1%
35432 1
< 0.1%
27847 1
< 0.1%
26859 1
< 0.1%
26695 1
< 0.1%
22879 1
< 0.1%
20901 1
< 0.1%

Interactions

2024-03-23T04:47:41.903533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:15.654991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:19.858921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:23.049691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:26.323192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:29.440856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:32.597739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:35.681334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:38.800283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:42.249248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:16.352605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:20.132188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:23.329493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:26.685593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:29.774351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:32.852159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:36.059832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:39.148535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:42.593867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:17.055940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:20.591559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:23.668375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:26.967702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:30.231317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:33.109936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:36.537521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:39.437768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:42.912925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:17.492515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:20.892272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:24.095596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:27.282087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:30.652175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:33.403309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:36.887953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:39.793973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:43.352896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:18.049389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:21.295797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:24.418529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:27.673249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:31.163247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:33.725451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:37.185055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:40.096753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:43.694030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:18.457469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:21.621109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:24.797077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:27.985295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:31.492101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:34.018297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:37.472619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:40.369364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:44.093259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:18.752186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:22.005616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:25.182214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:28.288639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:31.747650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:34.352977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:37.797560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:40.714419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:44.394053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:19.160074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:22.306404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:25.493902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:28.630586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:32.085028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:34.735796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:38.183622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:41.159459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:44.713825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:19.501342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:22.712704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:25.831201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:29.015698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:32.358435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:35.312149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:38.502368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:47:41.444102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T04:48:08.873157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도배출원대분류배출원중분류연료대분류연료소분류일산화탄소(CO)질소산화물(NOx)황산화물(SOx)총 부유입자(TSP)미세먼지(PM-10)초미세먼지(PM-2_5)휘발성유기화합물(VOC)암모니아(NH3)블랙카본(BC)
시도1.0000.1350.3180.1820.2960.3760.6750.0730.5340.4790.5610.1540.0220.766
배출원대분류0.1351.0001.0000.7530.6420.5030.0000.5220.2030.1110.0670.2040.2650.014
배출원중분류0.3181.0001.0000.8280.6440.8270.1270.8810.3990.3690.3460.6570.7440.153
연료대분류0.1820.7530.8281.0001.0000.2200.0000.3840.4680.6280.6650.000NaN0.000
연료소분류0.2960.6420.6441.0001.0000.3110.0000.4280.6250.6210.613NaNNaNNaN
일산화탄소(CO)0.3760.5030.8270.2200.3111.0000.6500.7980.7280.7010.8200.8091.0000.795
질소산화물(NOx)0.6750.0000.1270.0000.0000.6501.0000.0000.7180.7180.6760.2970.0000.943
황산화물(SOx)0.0730.5220.8810.3840.4280.7980.0001.0000.5560.4750.2780.4100.5270.000
총 부유입자(TSP)0.5340.2030.3990.4680.6250.7280.7180.5561.0000.8580.7450.6900.5190.791
미세먼지(PM-10)0.4790.1110.3690.6280.6210.7010.7180.4750.8581.0000.7690.6790.0000.727
초미세먼지(PM-2_5)0.5610.0670.3460.6650.6130.8200.6760.2780.7450.7691.0000.5480.0000.707
휘발성유기화합물(VOC)0.1540.2040.6570.000NaN0.8090.2970.4100.6900.6790.5481.0001.0000.443
암모니아(NH3)0.0220.2650.744NaNNaN1.0000.0000.5270.5190.0000.0001.0001.0000.000
블랙카본(BC)0.7660.0140.1530.000NaN0.7950.9430.0000.7910.7270.7070.4430.0001.000
2024-03-23T04:48:09.455523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출원대분류연료소분류연료대분류시도
배출원대분류1.0000.3800.5070.047
연료소분류0.3801.0000.9970.089
연료대분류0.5070.9971.0000.059
시도0.0470.0890.0591.000
2024-03-23T04:48:09.752025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일산화탄소(CO)질소산화물(NOx)황산화물(SOx)총 부유입자(TSP)미세먼지(PM-10)초미세먼지(PM-2_5)휘발성유기화합물(VOC)암모니아(NH3)블랙카본(BC)시도배출원대분류연료대분류연료소분류
일산화탄소(CO)1.0000.7920.7800.7960.7970.8030.9130.7200.8030.1770.2940.1070.150
질소산화물(NOx)0.7921.0000.7920.7510.7630.7650.7880.6990.7510.4070.0000.0000.000
황산화물(SOx)0.7800.7921.0000.7340.7310.7220.7510.7740.5420.0400.2540.3500.339
총 부유입자(TSP)0.7960.7510.7341.0000.9960.9780.8610.5370.7320.2050.0930.2350.365
미세먼지(PM-10)0.7970.7630.7310.9961.0000.9890.8630.5480.7700.2360.0560.3810.374
초미세먼지(PM-2_5)0.8030.7650.7220.9780.9891.0000.8710.5480.8410.3410.0400.3700.489
휘발성유기화합물(VOC)0.9130.7880.7510.8610.8630.8711.0000.6140.8580.0690.1020.0001.000
암모니아(NH3)0.7200.6990.7740.5370.5480.5480.6141.0000.4790.0100.1371.0001.000
블랙카본(BC)0.8030.7510.5420.7320.7700.8410.8580.4791.0000.4980.0080.0001.000
시도0.1770.4070.0400.2050.2360.3410.0690.0100.4981.0000.0470.0590.089
배출원대분류0.2940.0000.2540.0930.0560.0400.1020.1370.0080.0471.0000.5070.380
연료대분류0.1070.0000.3500.2350.3810.3700.0001.0000.0000.0590.5071.0000.997
연료소분류0.1500.0000.3390.3650.3740.4891.0001.0001.0000.0890.3800.9971.000

Missing values

2024-03-23T04:47:45.308999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T04:47:46.137420image/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.
2024-03-23T04:47:47.003570image/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

시도시군구배출원대분류배출원중분류배출원소분류연료대분류연료소분류일산화탄소(CO)질소산화물(NOx)황산화물(SOx)총 부유입자(TSP)미세먼지(PM-10)초미세먼지(PM-2_5)휘발성유기화합물(VOC)암모니아(NH3)블랙카본(BC)
11695울산광역시남구기타 면오염원동물인간<NA><NA><NA><NA><NA><NA><NA><NA><NA>78455<NA>
16319경기도과천시비산먼지도로재비산먼지승합차<NA><NA><NA><NA><NA>212540899<NA><NA>1
15843경기도고양시 일산동구제조업 연소기타출판, 인쇄 및 기록매체 복제업LPG프로판150000000
31560전라북도김제시제조업 연소기타목재 및 나무제품 제조업(가구 제외)LNGLNG8322810001130
44543경상남도거제시비도로이동오염원농업기계경운기경유<NA>27455491154954950582416390
1787서울특별시서대문구도로이동오염원승용차경형휘발유<NA>4664519443918181669231673
43023경상남도창원시 마산회원구비산먼지비포장도로 비산먼지버스<NA><NA><NA><NA><NA>1061<NA><NA>0
5349부산광역시금정구도로이동오염원이륜차260cc 이상휘발유<NA>7184979530302885320<NA>
43519경상남도통영시비산업 연소주거용시설기타등유실내등유238185733119117516411938116
11631울산광역시남구도로이동오염원버스시외버스경유<NA>572138611313123139
시도시군구배출원대분류배출원중분류배출원소분류연료대분류연료소분류일산화탄소(CO)질소산화물(NOx)황산화물(SOx)총 부유입자(TSP)미세먼지(PM-10)초미세먼지(PM-2_5)휘발성유기화합물(VOC)암모니아(NH3)블랙카본(BC)
10724대전광역시중구비산먼지도로재비산먼지버스<NA><NA><NA><NA><NA>84371620392<NA><NA>4
24573충청북도청주시 흥덕구도로이동오염원승용차경형LPG<NA>66516622221653<NA>
18116경기도용인시 수지구제조업 연소기타가죽,가방 및 신발 제조업LNGLNG000000000
30941전라북도익산시제조업 연소기타비금속광물제품 제조업B-A유B-A유(0.3%)56564424873390
14786경기도평택시제조업 연소기타코크스, 석유정제품 및 핵연료 제조업B-C유B-C유(0.3%)29321189332750
36798전라남도장성군제조업 연소기타가구 및 기타제품 제조업등유실내등유381530210460
31373전라북도남원시유기용제 사용도장시설나무,가구제조<NA><NA><NA><NA><NA><NA><NA><NA>28103<NA><NA>
28405충청남도논산시농업분뇨관리양 및 염소<NA><NA><NA><NA><NA><NA><NA><NA><NA>1460<NA>
19629경기도양주시비산업 연소상업 및 공공기관시설기타B-C유B-C유(0.3%)1812008118050451158290
30370전라북도전주시 완산구농업분뇨관리모피동물<NA><NA><NA><NA><NA><NA><NA><NA><NA>144<NA>

Duplicate rows

Most frequently occurring

시도시군구배출원대분류배출원중분류배출원소분류연료대분류연료소분류일산화탄소(CO)질소산화물(NOx)황산화물(SOx)총 부유입자(TSP)미세먼지(PM-10)초미세먼지(PM-2_5)휘발성유기화합물(VOC)암모니아(NH3)블랙카본(BC)# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>5