Overview

Dataset statistics

Number of variables6
Number of observations130
Missing cells30
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory49.0 B

Variable types

Text3
Categorical3

Dataset

Description2023년 1월 12일 기준, 나주시 소재의 폐기물 처리업체의 업체명, 업종구분, 소재지주소, 처리물질, 전화번호에 대한 정보를 제공합니다.
Author전라남도 나주시
URLhttps://www.data.go.kr/data/15025314/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
업종 is highly overall correlated with 처리물질High correlation
처리물질 is highly overall correlated with 업종High correlation
전화번호 has 30 (23.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 00:34:07.382712
Analysis finished2023-12-12 00:34:07.924271
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체명
Text

Distinct107
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:34:08.088543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.3
Min length3

Characters and Unicode

Total characters819
Distinct characters157
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)68.5%

Sample

1st row에코우드㈜
2nd row이진수지
3rd row(주)남부지엔씨
4th row㈜미래에코에너지
5th row왕곡농축영농조합법인
ValueCountFrequency (%)
유한회사 4
 
2.9%
㈜남부환경개발 3
 
2.2%
현대환경산업개발㈜ 3
 
2.2%
금성환경개발㈜ 3
 
2.2%
주)남부지엔씨 3
 
2.2%
㈜워렉스 3
 
2.2%
주식회사 3
 
2.2%
고려환경산업 2
 
1.4%
㈜창주산업 2
 
1.4%
㈜삼진산업 2
 
1.4%
Other values (101) 111
79.9%
2023-12-12T09:34:08.422854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 58
 
7.1%
산 35
 
4.3%
업 30
 
3.7%
유 28
 
3.4%
) 26
 
3.2%
( 26
 
3.2%
경 25
 
3.1%
환 24
 
2.9%
주 20
 
2.4%
지 17
 
2.1%
Other values (147) 530
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 700
85.5%
Other Symbol 58
 
7.1%
Close Punctuation 26
 
3.2%
Open Punctuation 26
 
3.2%
Space Separator 9
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 35
 
5.0%
업 30
 
4.3%
유 28
 
4.0%
경 25
 
3.6%
환 24
 
3.4%
주 20
 
2.9%
지 17
 
2.4%
스 15
 
2.1%
사 15
 
2.1%
개 13
 
1.9%
Other values (143) 478
68.3%
Other Symbol
ValueCountFrequency (%)
㈜ 58
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 758
92.6%
Common 61
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 58
 
7.7%
산 35
 
4.6%
업 30
 
4.0%
유 28
 
3.7%
경 25
 
3.3%
환 24
 
3.2%
주 20
 
2.6%
지 17
 
2.2%
스 15
 
2.0%
사 15
 
2.0%
Other values (144) 491
64.8%
Common
ValueCountFrequency (%)
) 26
42.6%
( 26
42.6%
9
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 700
85.5%
ASCII 61
 
7.4%
None 58
 
7.1%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 58
100.0%
Hangul
ValueCountFrequency (%)
산 35
 
5.0%
업 30
 
4.3%
유 28
 
4.0%
경 25
 
3.6%
환 24
 
3.4%
주 20
 
2.9%
지 17
 
2.4%
스 15
 
2.1%
사 15
 
2.1%
개 13
 
1.9%
Other values (143) 478
68.3%
ASCII
ValueCountFrequency (%)
) 26
42.6%
( 26
42.6%
9
 
14.8%

업종
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
수집운반업
49 
종합재활용업
38 
건설폐기물 수집운반업
18 
중간재활용업
12 
건설폐기물 중간처리업
10 

Length

Max length11
Median length6
Mean length6.7
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수집운반업 49
37.7%
종합재활용업 38
29.2%
건설폐기물 수집운반업 18
 
13.8%
중간재활용업 12
 
9.2%
건설폐기물 중간처리업 10
 
7.7%
최종재활용업 3
 
2.3%

Length

2023-12-12T09:34:08.531247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:34:08.623494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수집운반업 67
42.4%
종합재활용업 38
24.1%
건설폐기물 28
17.7%
중간재활용업 12
 
7.6%
중간처리업 10
 
6.3%
최종재활용업 3
 
1.9%

소재지주소
Text

Distinct99
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:34:08.896733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length26
Mean length21.376923
Min length15

Characters and Unicode

Total characters2779
Distinct characters100
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)59.2%

Sample

1st row전라남도 나주시 노안면 노안로 514-20
2nd row전라남도 나주시 노안면 노안삼도로 407-49
3rd row전라남도 나주시 다도면 다남로 107
4th row전라남도 나주시 봉황면 세남로 960-78
5th row전라남도 나주시 왕곡면 월천화정길 196-55
ValueCountFrequency (%)
전라남도 130
21.1%
나주시 130
21.1%
봉황면 15
 
2.4%
동수농공단지길 14
 
2.3%
다도면 12
 
1.9%
노안면 12
 
1.9%
왕곡면 11
 
1.8%
산포면 10
 
1.6%
남평읍 9
 
1.5%
세남로 8
 
1.3%
Other values (166) 265
43.0%
2023-12-12T09:34:09.344153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
503
18.1%
남 158
 
5.7%
도 148
 
5.3%
나 140
 
5.0%
주 140
 
5.0%
시 134
 
4.8%
전 130
 
4.7%
라 130
 
4.7%
1 122
 
4.4%
면 73
 
2.6%
Other values (90) 1101
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1683
60.6%
Decimal Number 518
 
18.6%
Space Separator 503
 
18.1%
Dash Punctuation 63
 
2.3%
Other Punctuation 7
 
0.3%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 158
 
9.4%
도 148
 
8.8%
나 140
 
8.3%
주 140
 
8.3%
시 134
 
8.0%
전 130
 
7.7%
라 130
 
7.7%
면 73
 
4.3%
로 73
 
4.3%
길 52
 
3.1%
Other values (74) 505
30.0%
Decimal Number
ValueCountFrequency (%)
1 122
23.6%
4 55
10.6%
0 54
10.4%
2 51
9.8%
3 48
 
9.3%
9 43
 
8.3%
7 38
 
7.3%
6 37
 
7.1%
8 37
 
7.1%
5 33
 
6.4%
Space Separator
ValueCountFrequency (%)
503
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1683
60.6%
Common 1095
39.4%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 158
 
9.4%
도 148
 
8.8%
나 140
 
8.3%
주 140
 
8.3%
시 134
 
8.0%
전 130
 
7.7%
라 130
 
7.7%
면 73
 
4.3%
로 73
 
4.3%
길 52
 
3.1%
Other values (74) 505
30.0%
Common
ValueCountFrequency (%)
503
45.9%
1 122
 
11.1%
- 63
 
5.8%
4 55
 
5.0%
0 54
 
4.9%
2 51
 
4.7%
3 48
 
4.4%
9 43
 
3.9%
7 38
 
3.5%
6 37
 
3.4%
Other values (5) 81
 
7.4%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1683
60.6%
ASCII 1096
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
503
45.9%
1 122
 
11.1%
- 63
 
5.7%
4 55
 
5.0%
0 54
 
4.9%
2 51
 
4.7%
3 48
 
4.4%
9 43
 
3.9%
7 38
 
3.5%
6 37
 
3.4%
Other values (6) 82
 
7.5%
Hangul
ValueCountFrequency (%)
남 158
 
9.4%
도 148
 
8.8%
나 140
 
8.3%
주 140
 
8.3%
시 134
 
8.0%
전 130
 
7.7%
라 130
 
7.7%
면 73
 
4.3%
로 73
 
4.3%
길 52
 
3.1%
Other values (74) 505
30.0%

처리물질
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
사업장배출시설계
29 
건설폐기물
28 
폐합성수지
19 
사업장비배출시설계
9 
폐합성수지류
5 
Other values (25)
40 

Length

Max length21
Median length19
Mean length6.9384615
Min length2

Unique

Unique16 ?
Unique (%)12.3%

Sample

1st row폐목재(고형연료)
2nd row폐합성수지
3rd row폐목재
4th row동식물성잔재물, 오니, 음식물류
5th row동식물성잔재물, 오니

Common Values

ValueCountFrequency (%)
사업장배출시설계 29
22.3%
건설폐기물 28
21.5%
폐합성수지 19
14.6%
사업장비배출시설계 9
 
6.9%
폐합성수지류 5
 
3.8%
폐목재 5
 
3.8%
오니 4
 
3.1%
음식물류 3
 
2.3%
동식물성잔재물, 오니 2
 
1.5%
무기성오니 2
 
1.5%
Other values (20) 24
18.5%

Length

2023-12-12T09:34:09.467370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사업장배출시설계 30
19.9%
건설폐기물 28
18.5%
폐합성수지 22
14.6%
오니 11
 
7.3%
사업장비배출시설계 10
 
6.6%
폐목재 7
 
4.6%
동물성잔재물 5
 
3.3%
폐합성수지류 5
 
3.3%
음식물류 5
 
3.3%
동식물성잔재물 5
 
3.3%
Other values (17) 23
15.2%

전화번호
Text

MISSING 

Distinct72
Distinct (%)72.0%
Missing30
Missing (%)23.1%
Memory size1.1 KiB
2023-12-12T09:34:09.684609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.03
Min length12

Characters and Unicode

Total characters1203
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

Unique52 ?
Unique (%)52.0%

Sample

1st row061-333-8970
2nd row061-337-4504
3rd row061-335-4580
4th row061-336-1230
5th row061-334-6633
ValueCountFrequency (%)
062-229-3530 5
 
5.0%
061-337-4504 4
 
4.0%
061-337-2407 3
 
3.0%
061-337-4506 3
 
3.0%
061-336-8827 3
 
3.0%
061-332-0797 2
 
2.0%
061-331-1314 2
 
2.0%
061-334-1616 2
 
2.0%
061-336-8866 2
 
2.0%
061-332-1330 2
 
2.0%
Other values (62) 72
72.0%
2023-12-12T09:34:10.001991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 237
19.7%
- 200
16.6%
0 176
14.6%
6 151
12.6%
1 128
10.6%
4 65
 
5.4%
7 59
 
4.9%
5 56
 
4.7%
2 53
 
4.4%
8 48
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1003
83.4%
Dash Punctuation 200
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 237
23.6%
0 176
17.5%
6 151
15.1%
1 128
12.8%
4 65
 
6.5%
7 59
 
5.9%
5 56
 
5.6%
2 53
 
5.3%
8 48
 
4.8%
9 30
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 237
19.7%
- 200
16.6%
0 176
14.6%
6 151
12.6%
1 128
10.6%
4 65
 
5.4%
7 59
 
4.9%
5 56
 
4.7%
2 53
 
4.4%
8 48
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 237
19.7%
- 200
16.6%
0 176
14.6%
6 151
12.6%
1 128
10.6%
4 65
 
5.4%
7 59
 
4.9%
5 56
 
4.7%
2 53
 
4.4%
8 48
 
4.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-01-12
130 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-12
2nd row2023-01-12
3rd row2023-01-12
4th row2023-01-12
5th row2023-01-12

Common Values

ValueCountFrequency (%)
2023-01-12 130
100.0%

Length

2023-12-12T09:34:10.109696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:34:10.181377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-12 130
100.0%

Correlations

2023-12-12T09:34:10.225589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종소재지주소처리물질전화번호
업종1.0000.7320.8760.825
소재지주소0.7321.0000.9580.999
처리물질0.8760.9581.0000.893
전화번호0.8250.9990.8931.000
2023-12-12T09:34:10.298932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리물질업종
처리물질1.0000.512
업종0.5121.000
2023-12-12T09:34:10.362119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종처리물질
업종1.0000.512
처리물질0.5121.000

Missing values

2023-12-12T09:34:07.811192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:34:07.892308image/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

업체명업종소재지주소처리물질전화번호데이터기준일자
0에코우드㈜종합재활용업전라남도 나주시 노안면 노안로 514-20폐목재(고형연료)061-333-89702023-01-12
1이진수지종합재활용업전라남도 나주시 노안면 노안삼도로 407-49폐합성수지<NA>2023-01-12
2(주)남부지엔씨종합재활용업전라남도 나주시 다도면 다남로 107폐목재061-337-45042023-01-12
3㈜미래에코에너지종합재활용업전라남도 나주시 봉황면 세남로 960-78동식물성잔재물, 오니, 음식물류061-335-45802023-01-12
4왕곡농축영농조합법인종합재활용업전라남도 나주시 왕곡면 월천화정길 196-55동식물성잔재물, 오니061-336-12302023-01-12
5현대환경산업개발㈜종합재활용업전라남도 나주시 봉황면 세남로 962-18폐목재061-334-66332023-01-12
6㈜테코종합재활용업전라남도 나주시 산포면 영산로 6009-25폐합성고무061-333-00882023-01-12
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