Overview

Dataset statistics

Number of variables13
Number of observations74
Missing cells30
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory107.8 B

Variable types

Text4
Categorical6
Boolean3

Dataset

Description울산광역시 남구 배출자율점검 지정업소 현황에 대한 데이터로 지정번호, 최초지정일자, 사업장명칭 등의 항목을 제공합니다.
Author울산광역시 남구
URLhttps://www.data.go.kr/data/3069045/fileData.do

Alerts

지정종료 is highly overall correlated with 최초지정일 and 3 other fieldsHigh correlation
폐기물 is highly overall correlated with 최초지정일 and 3 other fieldsHigh correlation
업종 is highly overall correlated with 수질 and 2 other fieldsHigh correlation
수질 is highly overall correlated with 최초지정일 and 7 other fieldsHigh correlation
지정일 is highly overall correlated with 최초지정일 and 3 other fieldsHigh correlation
재지정 is highly overall correlated with 최초지정일 and 3 other fieldsHigh correlation
최초지정일 is highly overall correlated with 지정일 and 5 other fieldsHigh correlation
대기 is highly overall correlated with 수질 and 2 other fieldsHigh correlation
기타수질 is highly overall correlated with 최초지정일 and 3 other fieldsHigh correlation
폐기물 is highly imbalanced (69.7%)Imbalance
대표자 has 30 (40.5%) missing valuesMissing
지정번호 has unique valuesUnique
사업장명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:29:43.366146
Analysis finished2023-12-12 16:29:44.604769
Duration1.24 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지정번호
Text

UNIQUE 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size724.0 B
2023-12-13T01:29:44.838468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters740
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)100.0%

Sample

1st row2022-04-01
2nd row2022-05-06
3rd row2022-06-06
4th row2022-07-06
5th row2022-09-06
ValueCountFrequency (%)
2022-04-01 1
 
1.4%
2022-08-17 1
 
1.4%
2022-06-17 1
 
1.4%
2022-05-17 1
 
1.4%
2022-04-17 1
 
1.4%
2022-02-17 1
 
1.4%
2022-01-17 1
 
1.4%
2015-13-01 1
 
1.4%
2022-12-15 1
 
1.4%
2022-11-15 1
 
1.4%
Other values (64) 64
86.5%
2023-12-13T01:29:45.231758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 218
29.5%
0 179
24.2%
- 148
20.0%
1 89
12.0%
7 31
 
4.2%
5 18
 
2.4%
6 17
 
2.3%
4 13
 
1.8%
8 13
 
1.8%
9 8
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 592
80.0%
Dash Punctuation 148
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 218
36.8%
0 179
30.2%
1 89
15.0%
7 31
 
5.2%
5 18
 
3.0%
6 17
 
2.9%
4 13
 
2.2%
8 13
 
2.2%
9 8
 
1.4%
3 6
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 218
29.5%
0 179
24.2%
- 148
20.0%
1 89
12.0%
7 31
 
4.2%
5 18
 
2.4%
6 17
 
2.3%
4 13
 
1.8%
8 13
 
1.8%
9 8
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 218
29.5%
0 179
24.2%
- 148
20.0%
1 89
12.0%
7 31
 
4.2%
5 18
 
2.4%
6 17
 
2.3%
4 13
 
1.8%
8 13
 
1.8%
9 8
 
1.1%

최초지정일
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Memory size724.0 B
2007-03-07
11 
2021-12-03
8 
2006-01-04
8 
2015-12-23
8 
2014-12-16
7 
Other values (16)
32 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique7 ?
Unique (%)9.5%

Sample

1st row2005-01-10
2nd row2006-01-04
3rd row2006-01-04
4th row2006-01-04
5th row2006-01-04

Common Values

ValueCountFrequency (%)
2007-03-07 11
14.9%
2021-12-03 8
10.8%
2006-01-04 8
10.8%
2015-12-23 8
10.8%
2014-12-16 7
9.5%
2010-11-23 5
 
6.8%
2017-01-12 3
 
4.1%
2007-08-31 3
 
4.1%
2020-12-31 3
 
4.1%
2017-01-02 3
 
4.1%
Other values (11) 15
20.3%

Length

2023-12-13T01:29:45.362426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2007-03-07 11
14.9%
2006-01-04 8
10.8%
2015-12-23 8
10.8%
2021-12-03 8
10.8%
2014-12-16 7
9.5%
2010-11-23 5
 
6.8%
2017-01-12 3
 
4.1%
2007-08-31 3
 
4.1%
2020-12-31 3
 
4.1%
2017-01-02 3
 
4.1%
Other values (11) 15
20.3%

사업장명칭
Text

UNIQUE 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size724.0 B
2023-12-13T01:29:45.565517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length9.0675676
Min length4

Characters and Unicode

Total characters671
Distinct characters192
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

Unique74 ?
Unique (%)100.0%

Sample

1st rowGS칼텍스㈜ 고속주유소
2nd row㈜대원고속(고속버스터미널)
3rd row㈜대원고속(시외버스터미널)
4th row즐거운주유소
5th row지에스칼텍스㈜ 평창점
ValueCountFrequency (%)
gs칼텍스㈜ 2
 
2.1%
울산점 2
 
2.1%
sk에너지㈜ 2
 
2.1%
울산개인택시제1충전소 1
 
1.0%
울산남구점 1
 
1.0%
홈플러스㈜ 1
 
1.0%
울산 1
 
1.0%
롯데호텔 1
 
1.0%
㈜호텔롯데 1
 
1.0%
롯데쇼핑㈜울산점 1
 
1.0%
Other values (84) 84
86.6%
2023-12-13T01:29:45.968546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 36
 
5.4%
유 31
 
4.6%
㈜ 30
 
4.5%
주 30
 
4.5%
24
 
3.6%
산 20
 
3.0%
울 18
 
2.7%
스 15
 
2.2%
지 12
 
1.8%
동 12
 
1.8%
Other values (182) 443
66.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 580
86.4%
Other Symbol 30
 
4.5%
Space Separator 24
 
3.6%
Uppercase Letter 20
 
3.0%
Close Punctuation 6
 
0.9%
Open Punctuation 6
 
0.9%
Decimal Number 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 36
 
6.2%
유 31
 
5.3%
주 30
 
5.2%
산 20
 
3.4%
울 18
 
3.1%
스 15
 
2.6%
지 12
 
2.1%
동 12
 
2.1%
비 12
 
2.1%
대 11
 
1.9%
Other values (171) 383
66.0%
Uppercase Letter
ValueCountFrequency (%)
S 7
35.0%
K 5
25.0%
G 4
20.0%
L 2
 
10.0%
P 2
 
10.0%
Decimal Number
ValueCountFrequency (%)
1 4
80.0%
2 1
 
20.0%
Other Symbol
ValueCountFrequency (%)
㈜ 30
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 610
90.9%
Common 41
 
6.1%
Latin 20
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 36
 
5.9%
유 31
 
5.1%
㈜ 30
 
4.9%
주 30
 
4.9%
산 20
 
3.3%
울 18
 
3.0%
스 15
 
2.5%
지 12
 
2.0%
동 12
 
2.0%
비 12
 
2.0%
Other values (172) 394
64.6%
Common
ValueCountFrequency (%)
24
58.5%
) 6
 
14.6%
( 6
 
14.6%
1 4
 
9.8%
2 1
 
2.4%
Latin
ValueCountFrequency (%)
S 7
35.0%
K 5
25.0%
G 4
20.0%
L 2
 
10.0%
P 2
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 580
86.4%
ASCII 61
 
9.1%
None 30
 
4.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 36
 
6.2%
유 31
 
5.3%
주 30
 
5.2%
산 20
 
3.4%
울 18
 
3.1%
스 15
 
2.6%
지 12
 
2.1%
동 12
 
2.1%
비 12
 
2.1%
대 11
 
1.9%
Other values (171) 383
66.0%
None
ValueCountFrequency (%)
㈜ 30
100.0%
ASCII
ValueCountFrequency (%)
24
39.3%
S 7
 
11.5%
) 6
 
9.8%
( 6
 
9.8%
K 5
 
8.2%
1 4
 
6.6%
G 4
 
6.6%
L 2
 
3.3%
P 2
 
3.3%
2 1
 
1.6%

업종
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size724.0 B
세차업
42 
자동차정비
9 
사진처리업
 
4
기술검사분석업
 
4
정비업
 
2
Other values (10)
13 

Length

Max length10
Median length3
Mean length3.8648649
Min length2

Unique

Unique7 ?
Unique (%)9.5%

Sample

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

Common Values

ValueCountFrequency (%)
세차업 42
56.8%
자동차정비 9
 
12.2%
사진처리업 4
 
5.4%
기술검사분석업 4
 
5.4%
정비업 2
 
2.7%
병원 2
 
2.7%
기타대형종합소매업 2
 
2.7%
보일러 2
 
2.7%
의원 1
 
1.4%
공공기관 1
 
1.4%
Other values (5) 5
 
6.8%

Length

2023-12-13T01:29:46.109493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
세차업 42
56.8%
자동차정비 9
 
12.2%
사진처리업 4
 
5.4%
기술검사분석업 4
 
5.4%
정비업 2
 
2.7%
병원 2
 
2.7%
기타대형종합소매업 2
 
2.7%
보일러 2
 
2.7%
의원 1
 
1.4%
공공기관 1
 
1.4%
Other values (5) 5
 
6.8%

대표자
Text

MISSING 

Distinct38
Distinct (%)86.4%
Missing30
Missing (%)40.5%
Memory size724.0 B
2023-12-13T01:29:46.355587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0681818
Min length2

Characters and Unicode

Total characters135
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)75.0%

Sample

1st row류*제
2nd row김*길
3rd row정*수
4th row김*숙
5th row이*자
ValueCountFrequency (%)
김*영 3
 
6.5%
백*선 2
 
4.3%
이*우 2
 
4.3%
김*섭 2
 
4.3%
김*길 2
 
4.3%
우*희 1
 
2.2%
김*국 1
 
2.2%
금*창 1
 
2.2%
임*식 1
 
2.2%
이*숙 1
 
2.2%
Other values (30) 30
65.2%
2023-12-13T01:29:46.735818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 44
32.6%
김 12
 
8.9%
이 7
 
5.2%
정 4
 
3.0%
박 4
 
3.0%
영 3
 
2.2%
백 3
 
2.2%
우 3
 
2.2%
섭 3
 
2.2%
숙 3
 
2.2%
Other values (39) 49
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
65.2%
Other Punctuation 44
32.6%
Space Separator 2
 
1.5%
Decimal Number 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 12
 
13.6%
이 7
 
8.0%
정 4
 
4.5%
박 4
 
4.5%
영 3
 
3.4%
백 3
 
3.4%
우 3
 
3.4%
섭 3
 
3.4%
숙 3
 
3.4%
태 3
 
3.4%
Other values (36) 43
48.9%
Other Punctuation
ValueCountFrequency (%)
* 44
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
65.2%
Common 47
34.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 12
 
13.6%
이 7
 
8.0%
정 4
 
4.5%
박 4
 
4.5%
영 3
 
3.4%
백 3
 
3.4%
우 3
 
3.4%
섭 3
 
3.4%
숙 3
 
3.4%
태 3
 
3.4%
Other values (36) 43
48.9%
Common
ValueCountFrequency (%)
* 44
93.6%
2
 
4.3%
1 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
65.2%
ASCII 47
34.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 44
93.6%
2
 
4.3%
1 1
 
2.1%
Hangul
ValueCountFrequency (%)
김 12
 
13.6%
이 7
 
8.0%
정 4
 
4.5%
박 4
 
4.5%
영 3
 
3.4%
백 3
 
3.4%
우 3
 
3.4%
섭 3
 
3.4%
숙 3
 
3.4%
태 3
 
3.4%
Other values (36) 43
48.9%

소재지도로명주소
Text

Distinct72
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size724.0 B
2023-12-13T01:29:47.028563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length13
Mean length14.054054
Min length11

Characters and Unicode

Total characters1040
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)94.6%

Sample

1st row울산 남구 문수로 279
2nd row울산 남구 화합로 133
3rd row울산 남구 화합로 133
4th row울산 남구 월평로 229
5th row울산 남구 돋질로 297
ValueCountFrequency (%)
울산 74
24.6%
남구 74
24.6%
화합로 7
 
2.3%
삼산로 7
 
2.3%
문수로 5
 
1.7%
두왕로 5
 
1.7%
수암로 4
 
1.3%
산업로 4
 
1.3%
26 3
 
1.0%
남산로 3
 
1.0%
Other values (97) 115
38.2%
2023-12-13T01:29:47.512252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
231
22.2%
산 100
 
9.6%
남 81
 
7.8%
구 75
 
7.2%
로 75
 
7.2%
울 74
 
7.1%
2 47
 
4.5%
1 41
 
3.9%
3 23
 
2.2%
6 22
 
2.1%
Other values (55) 271
26.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 578
55.6%
Space Separator 231
 
22.2%
Decimal Number 228
 
21.9%
Close Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 100
17.3%
남 81
14.0%
구 75
13.0%
로 75
13.0%
울 74
12.8%
번 17
 
2.9%
길 15
 
2.6%
삼 13
 
2.2%
수 10
 
1.7%
화 9
 
1.6%
Other values (41) 109
18.9%
Decimal Number
ValueCountFrequency (%)
2 47
20.6%
1 41
18.0%
3 23
10.1%
6 22
9.6%
4 20
8.8%
0 18
 
7.9%
9 17
 
7.5%
8 14
 
6.1%
7 14
 
6.1%
5 12
 
5.3%
Space Separator
ValueCountFrequency (%)
231
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 578
55.6%
Common 462
44.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 100
17.3%
남 81
14.0%
구 75
13.0%
로 75
13.0%
울 74
12.8%
번 17
 
2.9%
길 15
 
2.6%
삼 13
 
2.2%
수 10
 
1.7%
화 9
 
1.6%
Other values (41) 109
18.9%
Common
ValueCountFrequency (%)
231
50.0%
2 47
 
10.2%
1 41
 
8.9%
3 23
 
5.0%
6 22
 
4.8%
4 20
 
4.3%
0 18
 
3.9%
9 17
 
3.7%
8 14
 
3.0%
7 14
 
3.0%
Other values (4) 15
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 578
55.6%
ASCII 462
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
231
50.0%
2 47
 
10.2%
1 41
 
8.9%
3 23
 
5.0%
6 22
 
4.8%
4 20
 
4.3%
0 18
 
3.9%
9 17
 
3.7%
8 14
 
3.0%
7 14
 
3.0%
Other values (4) 15
 
3.2%
Hangul
ValueCountFrequency (%)
산 100
17.3%
남 81
14.0%
구 75
13.0%
로 75
13.0%
울 74
12.8%
번 17
 
2.9%
길 15
 
2.6%
삼 13
 
2.2%
수 10
 
1.7%
화 9
 
1.6%
Other values (41) 109
18.9%

지정일
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size724.0 B
2021-12-10
12 
2022-06-30
11 
2022-02-15
11 
2020-12-18
10 
2020-07-30
9 
Other values (7)
21 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)2.7%

Sample

1st row2021-12-10
2nd row2022-06-30
3rd row2022-06-30
4th row2022-06-30
5th row2022-06-30

Common Values

ValueCountFrequency (%)
2021-12-10 12
16.2%
2022-06-30 11
14.9%
2022-02-15 11
14.9%
2020-12-18 10
13.5%
2020-07-30 9
12.2%
2021-12-03 8
10.8%
2020-05-28 3
 
4.1%
2020-06-19 3
 
4.1%
2020-12-31 3
 
4.1%
2020-05-29 2
 
2.7%
Other values (2) 2
 
2.7%

Length

2023-12-13T01:29:47.653984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-12-10 12
16.2%
2022-06-30 11
14.9%
2022-02-15 11
14.9%
2020-12-18 10
13.5%
2020-07-30 9
12.2%
2021-12-03 8
10.8%
2020-05-28 3
 
4.1%
2020-06-19 3
 
4.1%
2020-12-31 3
 
4.1%
2020-05-29 2
 
2.7%
Other values (2) 2
 
2.7%

지정종료
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size724.0 B
2024-12-09
12 
2025-06-29
11 
2025-02-14
11 
2023-12-17
10 
2023-07-29
9 
Other values (7)
21 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)2.7%

Sample

1st row2024-12-09
2nd row2025-06-29
3rd row2025-06-29
4th row2025-06-29
5th row2025-06-29

Common Values

ValueCountFrequency (%)
2024-12-09 12
16.2%
2025-06-29 11
14.9%
2025-02-14 11
14.9%
2023-12-17 10
13.5%
2023-07-29 9
12.2%
2024-12-02 8
10.8%
2023-05-27 3
 
4.1%
2023-06-18 3
 
4.1%
2023-12-30 3
 
4.1%
2023-05-28 2
 
2.7%
Other values (2) 2
 
2.7%

Length

2023-12-13T01:29:47.786253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-12-09 12
16.2%
2025-06-29 11
14.9%
2025-02-14 11
14.9%
2023-12-17 10
13.5%
2023-07-29 9
12.2%
2024-12-02 8
10.8%
2023-05-27 3
 
4.1%
2023-06-18 3
 
4.1%
2023-12-30 3
 
4.1%
2023-05-28 2
 
2.7%
Other values (2) 2
 
2.7%

재지정
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size206.0 B
True
61 
False
13 
ValueCountFrequency (%)
True 61
82.4%
False 13
 
17.6%
2023-12-13T01:29:47.884823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

대기
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size724.0 B
<NA>
59 
4
11 
5
 
4

Length

Max length4
Median length4
Mean length3.3918919
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 59
79.7%
4 11
 
14.9%
5 4
 
5.4%

Length

2023-12-13T01:29:48.004553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:29:48.175786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 59
79.7%
4 11
 
14.9%
5 4
 
5.4%

수질
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
5
47 
<NA>
27 

Length

Max length4
Median length1
Mean length2.0945946
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 47
63.5%
<NA> 27
36.5%

Length

2023-12-13T01:29:48.317624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:29:48.435397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 47
63.5%
na 27
36.5%

기타수질
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size206.0 B
False
64 
True
10 
ValueCountFrequency (%)
False 64
86.5%
True 10
 
13.5%
2023-12-13T01:29:48.540615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

폐기물
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size206.0 B
False
70 
True
 
4
ValueCountFrequency (%)
False 70
94.6%
True 4
 
5.4%
2023-12-13T01:29:48.623637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:29:48.989973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호최초지정일사업장명칭업종대표자소재지도로명주소지정일지정종료재지정대기기타수질폐기물
지정번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
최초지정일1.0001.0001.0000.8380.8500.9870.9670.9671.0000.4990.7840.916
사업장명칭1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업종1.0000.8381.0001.0000.8760.0000.7110.7110.2540.2170.9591.000
대표자1.0000.8501.0000.8761.0000.9790.0000.0000.0001.0000.000NaN
소재지도로명주소1.0000.9871.0000.0000.9791.0000.9950.9951.0001.0001.0001.000
지정일1.0000.9671.0000.7110.0000.9951.0001.0000.9880.0000.6530.505
지정종료1.0000.9671.0000.7110.0000.9951.0001.0000.9880.0000.6530.505
재지정1.0001.0001.0000.2540.0001.0000.9880.9881.0000.0000.0940.000
대기1.0000.4991.0000.2171.0001.0000.0000.0000.0001.000NaNNaN
기타수질1.0000.7841.0000.9590.0001.0000.6530.6530.094NaN1.0000.000
폐기물1.0000.9161.0001.000NaN1.0000.5050.5050.000NaN0.0001.000
2023-12-13T01:29:49.129913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정종료폐기물업종수질지정일재지정최초지정일대기기타수질
지정종료1.0000.3630.3401.0001.0000.8400.7400.0000.476
폐기물0.3631.0000.9051.0000.3630.0000.7471.0000.000
업종0.3400.9051.0001.0000.3400.2040.4170.1870.872
수질1.0001.0001.0001.0001.0001.0001.0001.0001.000
지정일1.0000.3630.3401.0001.0000.8400.7400.0000.476
재지정0.8400.0000.2041.0000.8401.0000.8580.0000.058
최초지정일0.7400.7470.4171.0000.7400.8581.0000.1320.610
대기0.0001.0000.1871.0000.0000.0000.1321.0001.000
기타수질0.4760.0000.8721.0000.4760.0580.6101.0001.000
2023-12-13T01:29:49.256086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초지정일업종지정일지정종료재지정대기수질기타수질폐기물
최초지정일1.0000.4170.7400.7400.8580.1321.0000.6100.747
업종0.4171.0000.3400.3400.2040.1871.0000.8720.905
지정일0.7400.3401.0001.0000.8400.0001.0000.4760.363
지정종료0.7400.3401.0001.0000.8400.0001.0000.4760.363
재지정0.8580.2040.8400.8401.0000.0001.0000.0580.000
대기0.1320.1870.0000.0000.0001.0001.0001.0001.000
수질1.0001.0001.0001.0001.0001.0001.0001.0001.000
기타수질0.6100.8720.4760.4760.0581.0001.0001.0000.000
폐기물0.7470.9050.3630.3630.0001.0001.0000.0001.000

Missing values

2023-12-13T01:29:44.358526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:29:44.522057image/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

지정번호최초지정일사업장명칭업종대표자소재지도로명주소지정일지정종료재지정대기수질기타수질폐기물
02022-04-012005-01-10GS칼텍스㈜ 고속주유소세차업<NA>울산 남구 문수로 2792021-12-102024-12-09Y<NA>5NN
12022-05-062006-01-04㈜대원고속(고속버스터미널)세차업<NA>울산 남구 화합로 1332022-06-302025-06-29Y<NA>5NN
22022-06-062006-01-04㈜대원고속(시외버스터미널)세차업<NA>울산 남구 화합로 1332022-06-302025-06-29Y<NA>5NN
32022-07-062006-01-04즐거운주유소세차업류*제울산 남구 월평로 2292022-06-302025-06-29Y<NA>5NN
42022-09-062006-01-04지에스칼텍스㈜ 평창점세차업<NA>울산 남구 돋질로 2972022-06-302025-06-29Y<NA>5NN
52022-11-062006-01-04삼일주유소세차업김*길울산 남구 대학로 412022-06-302025-06-29Y<NA>5NN
62022-12-062006-01-04(주)투게더주유소세차업<NA>울산 남구 신화로 1012022-06-302025-06-29Y<NA>5NN
72006-13-012006-01-04동해주유소세차업정*수울산 남구 두왕로 382022-06-302025-06-29Y<NA>5NN
82006-16-012006-01-04완성주유소세차업김*숙울산 남구 온산로 8392022-06-302025-06-29Y<NA>5NN
92022-04-072007-03-07고바우주유소세차업이*자울산 남구 두왕로 2442020-07-302023-07-29Y<NA>5NN
지정번호최초지정일사업장명칭업종대표자소재지도로명주소지정일지정종료재지정대기수질기타수질폐기물
642022-01-212021-12-03SK에너지㈜스포츠센터종합스포츠시설운영업<NA>울산 남구 신선로 1662021-12-032024-12-02N4<NA>NN
652022-02-212021-12-03신대동주유소세차업김*영울산 남구 수암로 1992021-12-032024-12-02N<NA>5NN
662022-03-212021-12-03옥동셀프세차장세차업임*식울산 남구 은월로2번길 262021-12-032024-12-02N<NA>5NN
672022-04-212021-12-03에니카셀프세차장세차업금*창울산 남구 신정로 1202021-12-032024-12-02N<NA>5NN
682022-05-212021-12-03온산자동차서비스세차업김*국울산 남구 삼산로93번길 242021-12-032024-12-02N<NA>5NN
692022-06-212021-12-03프로카써비스세차업유*남울산 남구 돋질로92번길 52021-12-032024-12-02N<NA>5NN
702022-07-212021-12-03㈜옥동엘피지수소복합충전소세차업김*섭울산 남구 남부순환도로 4652021-12-032024-12-02N<NA>5NN
712022-08-212021-12-03내경의료재단울산제일병원병원김*길울산 남구 남산로354번길 262021-12-032024-12-02N<NA>5NN
722022-01-222022-06-30뉴동신정비공업사자동차정비이*태울산 남구 화합로178번길 262022-06-302025-06-29N4<NA>NN
732022-02-222022-06-30한국방송공사(울산방송국)지상파방송업<NA>울산 남구 번영로 2122022-06-302025-06-29N5<NA>NN