Overview

Dataset statistics

Number of variables13
Number of observations71
Missing cells71
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory109.9 B

Variable types

Numeric2
Categorical4
DateTime3
Text3
Unsupported1

Dataset

Description해당 데이터는 인천광역시 남동구의 배출 부과금 징수현황에 관련된 자료로서, 인천광역시 남동구 배출 부과금 징수현황의 년도, 분야, 구분, 부과일자, 업체명, 대표자, 소재지, 납부기한, 오염 물질명, 부과금액(원) , 가산금액 , 미수부과금액, 납부일의 정보를 확인할 수 있다.<br/>
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15104540&srcSe=7661IVAWM27C61E190

Alerts

분야 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
가산금액 is highly overall correlated with 년도 and 4 other fieldsHigh correlation
오염 물질명 is highly overall correlated with 부과금액(원) and 3 other fieldsHigh correlation
구분 is highly overall correlated with 부과금액(원) and 3 other fieldsHigh correlation
년도 is highly overall correlated with 가산금액 High correlation
부과금액(원) is highly overall correlated with 구분 and 2 other fieldsHigh correlation
가산금액 is highly imbalanced (80.1%)Imbalance
미수부과금액 has 71 (100.0%) missing valuesMissing
미수부과금액 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-06 09:38:58.868334
Analysis finished2024-04-06 09:39:00.652113
Duration1.78 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.2254
Minimum2006
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-04-06T18:39:00.716321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2007
Q12009
median2012
Q32021
95-th percentile2022
Maximum2022
Range16
Interquartile range (IQR)12

Descriptive statistics

Standard deviation5.8558815
Coefficient of variation (CV)0.0029072623
Kurtosis-1.5732836
Mean2014.2254
Median Absolute Deviation (MAD)5
Skewness0.14101899
Sum143010
Variance34.291348
MonotonicityIncreasing
2024-04-06T18:39:00.864644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2022 13
18.3%
2021 9
12.7%
2007 9
12.7%
2012 7
9.9%
2008 5
 
7.0%
2010 5
 
7.0%
2017 4
 
5.6%
2011 4
 
5.6%
2006 3
 
4.2%
2014 3
 
4.2%
Other values (6) 9
12.7%
ValueCountFrequency (%)
2006 3
 
4.2%
2007 9
12.7%
2008 5
7.0%
2009 3
 
4.2%
2010 5
7.0%
2011 4
5.6%
2012 7
9.9%
2013 1
 
1.4%
2014 3
 
4.2%
2015 1
 
1.4%
ValueCountFrequency (%)
2022 13
18.3%
2021 9
12.7%
2019 1
 
1.4%
2018 2
 
2.8%
2017 4
 
5.6%
2016 1
 
1.4%
2015 1
 
1.4%
2014 3
 
4.2%
2013 1
 
1.4%
2012 7
9.9%

분야
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
수질
43 
대기
28 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수질
2nd row대기
3rd row수질
4th row대기
5th row대기

Common Values

ValueCountFrequency (%)
수질 43
60.6%
대기 28
39.4%

Length

2024-04-06T18:39:01.066813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:39:01.287629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수질 43
60.6%
대기 28
39.4%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size700.0 B
초과
39 
기본
31 
과징금
 
1

Length

Max length3
Median length2
Mean length2.0140845
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row초과
2nd row기본
3rd row기본
4th row초과
5th row기본

Common Values

ValueCountFrequency (%)
초과 39
54.9%
기본 31
43.7%
과징금 1
 
1.4%

Length

2024-04-06T18:39:01.488877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:39:01.628336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
초과 39
54.9%
기본 31
43.7%
과징금 1
 
1.4%

부과일자
Date

Distinct51
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
Minimum2006-04-04 00:00:00
Maximum2022-09-30 00:00:00
2024-04-06T18:39:01.755938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:39:01.894928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업체명
Text

Distinct46
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-04-06T18:39:02.106793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length6.5492958
Min length2

Characters and Unicode

Total characters465
Distinct characters135
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)50.7%

Sample

1st row해안세차장
2nd row㈜경남산업
3rd row㈜하얀나라
4th row신태영자동차공업사
5th row㈜경남산업
ValueCountFrequency (%)
㈜경남산업 12
 
16.2%
주)경남산업 4
 
5.4%
소래포구전통어시장 3
 
4.1%
의료)길의료재단 3
 
4.1%
길의료재단중앙길병원 3
 
4.1%
㈜하얀나라 3
 
4.1%
워시홀릭파크 2
 
2.7%
한라자동차공업사 2
 
2.7%
아세아바렐 2
 
2.7%
신태영자동차공업사 2
 
2.7%
Other values (38) 38
51.4%
2024-04-06T18:39:02.525760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 25
 
5.4%
업 25
 
5.4%
산 19
 
4.1%
남 17
 
3.7%
경 16
 
3.4%
주 11
 
2.4%
장 10
 
2.2%
( 9
 
1.9%
길 9
 
1.9%
차 9
 
1.9%
Other values (125) 315
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 413
88.8%
Other Symbol 25
 
5.4%
Open Punctuation 10
 
2.2%
Close Punctuation 10
 
2.2%
Space Separator 3
 
0.6%
Uppercase Letter 3
 
0.6%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 25
 
6.1%
산 19
 
4.6%
남 17
 
4.1%
경 16
 
3.9%
주 11
 
2.7%
장 10
 
2.4%
길 9
 
2.2%
차 9
 
2.2%
소 9
 
2.2%
의 9
 
2.2%
Other values (115) 279
67.6%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
S 1
33.3%
M 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 9
90.0%
[ 1
 
10.0%
Close Punctuation
ValueCountFrequency (%)
) 9
90.0%
] 1
 
10.0%
Other Symbol
ValueCountFrequency (%)
㈜ 25
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 438
94.2%
Common 24
 
5.2%
Latin 3
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 25
 
5.7%
업 25
 
5.7%
산 19
 
4.3%
남 17
 
3.9%
경 16
 
3.7%
주 11
 
2.5%
장 10
 
2.3%
길 9
 
2.1%
차 9
 
2.1%
소 9
 
2.1%
Other values (116) 288
65.8%
Common
ValueCountFrequency (%)
( 9
37.5%
) 9
37.5%
3
 
12.5%
] 1
 
4.2%
1 1
 
4.2%
[ 1
 
4.2%
Latin
ValueCountFrequency (%)
T 1
33.3%
S 1
33.3%
M 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 413
88.8%
ASCII 27
 
5.8%
None 25
 
5.4%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 25
100.0%
Hangul
ValueCountFrequency (%)
업 25
 
6.1%
산 19
 
4.6%
남 17
 
4.1%
경 16
 
3.9%
주 11
 
2.7%
장 10
 
2.4%
길 9
 
2.2%
차 9
 
2.2%
소 9
 
2.2%
의 9
 
2.2%
Other values (115) 279
67.6%
ASCII
ValueCountFrequency (%)
( 9
33.3%
) 9
33.3%
3
 
11.1%
] 1
 
3.7%
1 1
 
3.7%
[ 1
 
3.7%
T 1
 
3.7%
S 1
 
3.7%
M 1
 
3.7%

대표자
Text

Distinct39
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-04-06T18:39:02.770005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.6056338
Min length3

Characters and Unicode

Total characters256
Distinct characters68
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)43.7%

Sample

1st row강희남
2nd row이해용
3rd row김남호
4th row방석교
5th row이해용
ValueCountFrequency (%)
대표이사 22
29.7%
이해용 6
 
8.1%
김남호 3
 
4.1%
임기택 2
 
2.7%
방석교 2
 
2.7%
양해남 2
 
2.7%
이상현 2
 
2.7%
구청장(이강호 2
 
2.7%
외 1
 
1.4%
남동구청장 1
 
1.4%
Other values (31) 31
41.9%
2024-04-06T18:39:03.118025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 42
 
16.4%
대 22
 
8.6%
표 22
 
8.6%
사 22
 
8.6%
해 8
 
3.1%
용 7
 
2.7%
남 7
 
2.7%
호 7
 
2.7%
김 6
 
2.3%
최 4
 
1.6%
Other values (58) 109
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 248
96.9%
Space Separator 3
 
1.2%
Close Punctuation 2
 
0.8%
Open Punctuation 2
 
0.8%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 42
16.9%
대 22
 
8.9%
표 22
 
8.9%
사 22
 
8.9%
해 8
 
3.2%
용 7
 
2.8%
남 7
 
2.8%
호 7
 
2.8%
김 6
 
2.4%
최 4
 
1.6%
Other values (54) 101
40.7%
Space Separator
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 248
96.9%
Common 8
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 42
16.9%
대 22
 
8.9%
표 22
 
8.9%
사 22
 
8.9%
해 8
 
3.2%
용 7
 
2.8%
남 7
 
2.8%
호 7
 
2.8%
김 6
 
2.4%
최 4
 
1.6%
Other values (54) 101
40.7%
Common
ValueCountFrequency (%)
3
37.5%
) 2
25.0%
( 2
25.0%
1 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 248
96.9%
ASCII 8
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 42
16.9%
대 22
 
8.9%
표 22
 
8.9%
사 22
 
8.9%
해 8
 
3.2%
용 7
 
2.8%
남 7
 
2.8%
호 7
 
2.8%
김 6
 
2.4%
최 4
 
1.6%
Other values (54) 101
40.7%
ASCII
ValueCountFrequency (%)
3
37.5%
) 2
25.0%
( 2
25.0%
1 1
 
12.5%

소재지
Text

Distinct46
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-04-06T18:39:03.373308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length11.690141
Min length7

Characters and Unicode

Total characters830
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)56.3%

Sample

1st row고잔동 523
2nd row고잔동 120-3
3rd row고잔동 86-8
4th row간석동 616-61
5th row고잔동 120-3
ValueCountFrequency (%)
고잔동 23
 
15.4%
120-3 16
 
10.7%
간석동 9
 
6.0%
남동대로774번길 5
 
3.4%
21 4
 
2.7%
616-61 4
 
2.7%
장도로 3
 
2.0%
86-14 3
 
2.0%
청능대로468번길 3
 
2.0%
청능대로 2
 
1.3%
Other values (70) 77
51.7%
2024-04-06T18:39:04.135946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78
 
9.4%
1 67
 
8.1%
동 66
 
8.0%
3 49
 
5.9%
- 48
 
5.8%
2 40
 
4.8%
고 39
 
4.7%
6 37
 
4.5%
잔 36
 
4.3%
로 34
 
4.1%
Other values (47) 336
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 344
41.4%
Decimal Number 317
38.2%
Space Separator 78
 
9.4%
Dash Punctuation 48
 
5.8%
Open Punctuation 21
 
2.5%
Close Punctuation 21
 
2.5%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 66
19.2%
고 39
11.3%
잔 36
10.5%
로 34
9.9%
번 22
 
6.4%
길 22
 
6.4%
대 16
 
4.7%
석 14
 
4.1%
간 13
 
3.8%
남 10
 
2.9%
Other values (32) 72
20.9%
Decimal Number
ValueCountFrequency (%)
1 67
21.1%
3 49
15.5%
2 40
12.6%
6 37
11.7%
4 33
10.4%
0 24
 
7.6%
7 22
 
6.9%
8 21
 
6.6%
5 13
 
4.1%
9 11
 
3.5%
Space Separator
ValueCountFrequency (%)
78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 486
58.6%
Hangul 344
41.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 66
19.2%
고 39
11.3%
잔 36
10.5%
로 34
9.9%
번 22
 
6.4%
길 22
 
6.4%
대 16
 
4.7%
석 14
 
4.1%
간 13
 
3.8%
남 10
 
2.9%
Other values (32) 72
20.9%
Common
ValueCountFrequency (%)
78
16.0%
1 67
13.8%
3 49
10.1%
- 48
9.9%
2 40
8.2%
6 37
7.6%
4 33
6.8%
0 24
 
4.9%
7 22
 
4.5%
( 21
 
4.3%
Other values (5) 67
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 486
58.6%
Hangul 344
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
78
16.0%
1 67
13.8%
3 49
10.1%
- 48
9.9%
2 40
8.2%
6 37
7.6%
4 33
6.8%
0 24
 
4.9%
7 22
 
4.5%
( 21
 
4.3%
Other values (5) 67
13.8%
Hangul
ValueCountFrequency (%)
동 66
19.2%
고 39
11.3%
잔 36
10.5%
로 34
9.9%
번 22
 
6.4%
길 22
 
6.4%
대 16
 
4.7%
석 14
 
4.1%
간 13
 
3.8%
남 10
 
2.9%
Other values (32) 72
20.9%

납부기한
Date

Distinct56
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size700.0 B
Minimum2006-05-08 00:00:00
Maximum2022-10-31 00:00:00
2024-04-06T18:39:04.299983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:39:04.467987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

오염 물질명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
먼지
21 
COD
14 
TOC
6 
질소산화물
5 
유기물질(COD)
4 
Other values (13)
21 

Length

Max length19
Median length12
Mean length4.2253521
Min length2

Unique

Unique8 ?
Unique (%)11.3%

Sample

1st rowCOD
2nd row먼지
3rd rowCOD
4th row먼지
5th row먼지

Common Values

ValueCountFrequency (%)
먼지 21
29.6%
COD 14
19.7%
TOC 6
 
8.5%
질소산화물 5
 
7.0%
유기물질(COD) 4
 
5.6%
BOD, SS 3
 
4.2%
총인 3
 
4.2%
부유물질(SS) 3
 
4.2%
SS 2
 
2.8%
COD, SS 2
 
2.8%
Other values (8) 8
 
11.3%

Length

2024-04-06T18:39:04.633498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
먼지 22
26.5%
cod 16
19.3%
ss 7
 
8.4%
toc 6
 
7.2%
질소산화물 6
 
7.2%
유기물질(cod 4
 
4.8%
bod 4
 
4.8%
총인 4
 
4.8%
부유물질(ss 3
 
3.6%
총질소 2
 
2.4%
Other values (8) 9
10.8%

부과금액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1495503
Minimum14170
Maximum45000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-04-06T18:39:04.804690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14170
5-th percentile37555
Q1213665
median500000
Q3666805
95-th percentile3863385
Maximum45000000
Range44985830
Interquartile range (IQR)453140

Descriptive statistics

Standard deviation5662791.8
Coefficient of variation (CV)3.7865467
Kurtosis51.912937
Mean1495503
Median Absolute Deviation (MAD)268150
Skewness6.9595334
Sum1.0618071 × 108
Variance3.2067211 × 1013
MonotonicityNot monotonic
2024-04-06T18:39:04.983537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500000 15
 
21.1%
500830 1
 
1.4%
231850 1
 
1.4%
524500 1
 
1.4%
1469160 1
 
1.4%
40730 1
 
1.4%
1197240 1
 
1.4%
512670 1
 
1.4%
661890 1
 
1.4%
509620 1
 
1.4%
Other values (47) 47
66.2%
ValueCountFrequency (%)
14170 1
1.4%
19410 1
1.4%
29460 1
1.4%
34380 1
1.4%
40730 1
1.4%
48500 1
1.4%
59890 1
1.4%
82330 1
1.4%
84870 1
1.4%
104760 1
1.4%
ValueCountFrequency (%)
45000000 1
1.4%
16709670 1
1.4%
6504610 1
1.4%
5767940 1
1.4%
1958830 1
1.4%
1469160 1
1.4%
1197240 1
1.4%
1073450 1
1.4%
1011230 1
1.4%
896060 1
1.4%

가산금액
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size700.0 B
<NA>
67 
15000
 
2
50560
 
1
501290
 
1

Length

Max length6
Median length4
Mean length4.0704225
Min length4

Unique

Unique2 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 67
94.4%
15000 2
 
2.8%
50560 1
 
1.4%
501290 1
 
1.4%

Length

2024-04-06T18:39:05.159712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:39:05.301716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 67
94.4%
15000 2
 
2.8%
50560 1
 
1.4%
501290 1
 
1.4%

미수부과금액
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing71
Missing (%)100.0%
Memory size771.0 B

납부일
Date

Distinct64
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size700.0 B
Minimum2006-04-14 00:00:00
Maximum2022-10-29 00:00:00
2024-04-06T18:39:05.448112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:39:05.647064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-04-06T18:39:00.077366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:38:59.801458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:39:00.197296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:38:59.941147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T18:39:05.774873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도분야구분부과일자업체명대표자소재지납부기한오염 물질명부과금액(원)가산금액납부일
년도1.0000.4060.6131.0000.9660.7940.9541.0000.7460.4241.0001.000
분야0.4061.0000.4140.0000.9950.8620.9880.2781.0000.130NaN0.598
구분0.6130.4141.0000.9740.9580.0000.9551.0000.9930.678NaN0.983
부과일자1.0000.0000.9741.0000.9730.9650.9761.0000.7970.7371.0000.999
업체명0.9660.9950.9580.9731.0000.9980.9990.9760.9930.9591.0000.968
대표자0.7940.8620.0000.9650.9981.0000.9970.9720.9320.0001.0000.994
소재지0.9540.9880.9550.9760.9990.9971.0000.9840.9980.9591.0000.988
납부기한1.0000.2781.0001.0000.9760.9720.9841.0000.8260.0001.0000.997
오염 물질명0.7461.0000.9930.7970.9930.9320.9980.8261.0000.9761.0000.865
부과금액(원)0.4240.1300.6780.7370.9590.0000.9590.0000.9761.0001.0000.000
가산금액1.000NaNNaN1.0001.0001.0001.0001.0001.0001.0001.0001.000
납부일1.0000.5980.9830.9990.9680.9940.9880.9970.8650.0001.0001.000
2024-04-06T18:39:05.914497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분야가산금액오염 물질명구분
분야1.0001.0000.8760.645
가산금액1.0001.0001.0001.000
오염 물질명0.8761.0001.0000.795
구분0.6451.0000.7951.000
2024-04-06T18:39:06.060658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도부과금액(원)분야구분오염 물질명가산금액
년도1.0000.1330.2820.4380.3641.000
부과금액(원)0.1331.0000.0820.7040.8190.707
분야0.2820.0821.0000.6450.8761.000
구분0.4380.7040.6451.0000.7951.000
오염 물질명0.3640.8190.8760.7951.0001.000
가산금액1.0000.7071.0001.0001.0001.000

Missing values

2024-04-06T18:39:00.366202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T18:39:00.579919image/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

년도분야구분부과일자업체명대표자소재지납부기한오염 물질명부과금액(원)가산금액미수부과금액납부일
02006수질초과2006-04-04해안세차장강희남고잔동 5232006-05-08COD500830<NA><NA>2006-04-14
12006대기기본2006-09-09㈜경남산업이해용고잔동 120-32006-10-25먼지212120<NA><NA>2006-10-25
22006수질기본2006-09-09㈜하얀나라김남호고잔동 86-82006-10-25COD48500<NA><NA>2006-10-12
32007대기초과2007-02-02신태영자동차공업사방석교간석동 616-612007-03-05먼지19410<NA><NA>2007-03-05
42007대기기본2007-03-03㈜경남산업이해용고잔동 120-32007-04-27먼지693960<NA><NA>2007-04-10
52007수질기본2007-03-03㈜하얀나라김남호고잔동 86-82007-04-27COD84870<NA><NA>2007-04-03
62007수질기본2007-03-03길의료재단중앙길병원이창규구월동 11982007-04-27BOD59890<NA><NA>2007-04-24
72007대기기본2007-09-09㈜경남산업이해용고잔동 120-32007-10-28먼지229560<NA><NA>2007-10-09
82007대기기본2007-09-09신태영자동차공업사방석교간석동 616-612007-10-28먼지14170<NA><NA>2007-10-22
92007수질기본2007-09-09세원산업김기만고잔동 9712007-10-28SS1958830<NA><NA>2007-10-29
년도분야구분부과일자업체명대표자소재지납부기한오염 물질명부과금액(원)가산금액미수부과금액납부일
612022수질초과2022-04-07㈜엠알메탈로 1공장대표이사앵고개로719번길 84(고잔동)2022-06-06Cu, T-N, ABS16709670501290<NA>2022-06-03
622022수질초과2022-04-28내일비안와요하기종문화로245번길 33(간석동)2022-05-27TOC500000<NA><NA>2022-05-27
632022수질초과2022-08-23워시홀릭파크양해남청능대로468번길 13(고잔동)2022-09-26TOC671720<NA><NA>2022-09-26
642022수질초과2022-08-23워시홀릭파크양해남청능대로468번길 13(고잔동)2022-09-26TOC500000<NA><NA>2022-09-26
652022수질초과2022-09-05서광주유소정은주백범로 471(간석동)2022-10-04TOC500000<NA><NA>2022-10-04
662022수질기본2022-09-30소래포구전통어시장남동구청장장도로 86-142022-10-31BOD, SS5767940<NA><NA>2022-10-29
672022대기기본2022-09-30(의료)길의료재단대표이사남동대로774번길 212022-10-31질소산화물191290<NA><NA>2022-10-29
682022대기기본2022-09-30(의료)길의료재단 [암센터]대표이사남동대로774번길 212022-10-31질소산화물215210<NA><NA>2022-10-29
692022대기기본2022-09-30㈜장원(인천공장지점)대표이사남동대로262번길 30-37(논현동)2022-10-31질소산화물, 먼지267750<NA><NA>2022-10-29
702022대기기본2022-09-30㈜예림임업대표이사 이상백고잔동 246-1(고잔동)2022-10-31먼지111400<NA><NA>2022-10-29