Overview

Dataset statistics

Number of variables6
Number of observations95
Missing cells12
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory50.4 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description폐기물관리법 제25조, 제29조, 제46조에 따른 폐기물처리업체, 폐기물처리시설 설치업체 및 폐기물처리신고업체와 건설폐기물재활용촉진에관한법률 제22조에 따른 건설폐기물처리업체 등 부산광역시 사상구의 폐기물업체 현황을 정보공개하고자 합니다.
Author부산광역시 사상구
URLhttps://www.data.go.kr/data/15025670/fileData.do

Alerts

비고 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
업종 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 업종 and 1 other fieldsHigh correlation
비고 is highly imbalanced (74.8%)Imbalance
연락처 has 12 (12.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:34:56.108720
Analysis finished2024-04-06 08:34:57.470593
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2024-04-06T17:34:57.628393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.7
Q124.5
median48
Q371.5
95-th percentile90.3
Maximum95
Range94
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.568098
Coefficient of variation (CV)0.57433536
Kurtosis-1.2
Mean48
Median Absolute Deviation (MAD)24
Skewness0
Sum4560
Variance760
MonotonicityStrictly increasing
2024-04-06T17:34:57.860945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
2 1
 
1.1%
71 1
 
1.1%
70 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
Other values (85) 85
89.5%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
95 1
1.1%
94 1
1.1%
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%

업종
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size892.0 B
수집운반(사업장배출시설계)
37 
수집운반(건설)
15 
수집운반(사업장비배출시설계)
8 
중간재활용(사업장)
7 
종합재활용(사업장)
7 
Other values (6)
21 

Length

Max length15
Median length14
Mean length11.368421
Min length8

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row수집운반(건설)
2nd row수집운반(건설)
3rd row수집운반(건설)
4th row수집운반(건설)
5th row수집운반(건설)

Common Values

ValueCountFrequency (%)
수집운반(사업장배출시설계) 37
38.9%
수집운반(건설) 15
15.8%
수집운반(사업장비배출시설계) 8
 
8.4%
중간재활용(사업장) 7
 
7.4%
종합재활용(사업장) 7
 
7.4%
시설(압축시설) 7
 
7.4%
수집운반(생활) 4
 
4.2%
중간처리(건설) 4
 
4.2%
처리신고(수집운반) 3
 
3.2%
처리신고(재활용) 2
 
2.1%

Length

2024-04-06T17:34:58.093398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수집운반(사업장배출시설계 37
38.5%
수집운반(건설 15
15.6%
수집운반(사업장비배출시설계 8
 
8.3%
중간재활용(사업장 7
 
7.3%
종합재활용(사업장 7
 
7.3%
시설(압축시설 7
 
7.3%
수집운반(생활 4
 
4.2%
중간처리(건설 4
 
4.2%
처리신고(수집운반 3
 
3.1%
처리신고(재활용 2
 
2.1%
Other values (2) 2
 
2.1%

사업장명
Text

Distinct80
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Memory size892.0 B
2024-04-06T17:34:58.449464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length7.1789474
Min length2

Characters and Unicode

Total characters682
Distinct characters117
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

Unique66 ?
Unique (%)69.5%

Sample

1st row(주)동아에너지
2nd row(주)풍영(임시보관장소)
3rd row부산리사이클링(주)
4th row호제환경산업(주)
5th row금강산개발(주)(임시보관장소)
ValueCountFrequency (%)
주)호생환경 3
 
3.1%
대성기업(주 2
 
2.1%
주)삼정환경 2
 
2.1%
주)아진산업 2
 
2.1%
우호기업 2
 
2.1%
주)이놉스 2
 
2.1%
청신산업(주 2
 
2.1%
주)대흥리사이클링-폐지 2
 
2.1%
에코포스트(주 2
 
2.1%
주)삼정환경산업 2
 
2.1%
Other values (71) 75
78.1%
2024-04-06T17:34:59.497462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 56
 
8.2%
) 56
 
8.2%
주 55
 
8.1%
경 32
 
4.7%
산 31
 
4.5%
업 29
 
4.3%
환 29
 
4.3%
이 16
 
2.3%
대 15
 
2.2%
리 12
 
1.8%
Other values (107) 351
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 556
81.5%
Open Punctuation 56
 
8.2%
Close Punctuation 56
 
8.2%
Dash Punctuation 12
 
1.8%
Space Separator 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 55
 
9.9%
경 32
 
5.8%
산 31
 
5.6%
업 29
 
5.2%
환 29
 
5.2%
이 16
 
2.9%
대 15
 
2.7%
리 12
 
2.2%
폐 11
 
2.0%
원 11
 
2.0%
Other values (103) 315
56.7%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 556
81.5%
Common 126
 
18.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 55
 
9.9%
경 32
 
5.8%
산 31
 
5.6%
업 29
 
5.2%
환 29
 
5.2%
이 16
 
2.9%
대 15
 
2.7%
리 12
 
2.2%
폐 11
 
2.0%
원 11
 
2.0%
Other values (103) 315
56.7%
Common
ValueCountFrequency (%)
( 56
44.4%
) 56
44.4%
- 12
 
9.5%
2
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 556
81.5%
ASCII 126
 
18.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 56
44.4%
) 56
44.4%
- 12
 
9.5%
2
 
1.6%
Hangul
ValueCountFrequency (%)
주 55
 
9.9%
경 32
 
5.8%
산 31
 
5.6%
업 29
 
5.2%
환 29
 
5.2%
이 16
 
2.9%
대 15
 
2.7%
리 12
 
2.2%
폐 11
 
2.0%
원 11
 
2.0%
Other values (103) 315
56.7%

소재지
Text

Distinct74
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Memory size892.0 B
2024-04-06T17:34:59.979040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length33
Mean length18.705263
Min length10

Characters and Unicode

Total characters1777
Distinct characters102
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

Unique58 ?
Unique (%)61.1%

Sample

1st row낙동대로1062번길 17(감전동)
2nd row장인로37번길 116(감전동)
3rd row낙동대로901번길 25-18(감전동)
4th row낙동대로 910, E315호(감전동, 마트월드)
5th row낙동대로1310번길 63(삼락동)
ValueCountFrequency (%)
낙동대로 17
 
6.7%
광장로 5
 
2.0%
새벽로 5
 
2.0%
모덕로 4
 
1.6%
101-1(괘법동 4
 
1.6%
910 4
 
1.6%
마트월드 4
 
1.6%
새벽로77번길 3
 
1.2%
가야대로 3
 
1.2%
감전천로 3
 
1.2%
Other values (156) 201
79.4%
2024-04-06T17:35:00.744350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
158
 
8.9%
동 138
 
7.8%
1 102
 
5.7%
( 96
 
5.4%
) 96
 
5.4%
로 95
 
5.3%
2 69
 
3.9%
0 63
 
3.5%
번 56
 
3.2%
3 54
 
3.0%
Other values (92) 850
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 881
49.6%
Decimal Number 485
27.3%
Space Separator 158
 
8.9%
Open Punctuation 96
 
5.4%
Close Punctuation 96
 
5.4%
Other Punctuation 33
 
1.9%
Dash Punctuation 17
 
1.0%
Uppercase Letter 6
 
0.3%
Lowercase Letter 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 138
 
15.7%
로 95
 
10.8%
번 56
 
6.4%
길 53
 
6.0%
대 38
 
4.3%
감 33
 
3.7%
장 32
 
3.6%
전 31
 
3.5%
낙 27
 
3.1%
학 20
 
2.3%
Other values (69) 358
40.6%
Decimal Number
ValueCountFrequency (%)
1 102
21.0%
2 69
14.2%
0 63
13.0%
3 54
11.1%
6 43
8.9%
5 38
 
7.8%
4 37
 
7.6%
7 36
 
7.4%
9 25
 
5.2%
8 18
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
t 1
20.0%
r 1
20.0%
n 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 2
33.3%
A 2
33.3%
E 1
16.7%
D 1
16.7%
Space Separator
ValueCountFrequency (%)
158
100.0%
Open Punctuation
ValueCountFrequency (%)
( 96
100.0%
Close Punctuation
ValueCountFrequency (%)
) 96
100.0%
Other Punctuation
ValueCountFrequency (%)
, 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 885
49.8%
Hangul 881
49.6%
Latin 11
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 138
 
15.7%
로 95
 
10.8%
번 56
 
6.4%
길 53
 
6.0%
대 38
 
4.3%
감 33
 
3.7%
장 32
 
3.6%
전 31
 
3.5%
낙 27
 
3.1%
학 20
 
2.3%
Other values (69) 358
40.6%
Common
ValueCountFrequency (%)
158
17.9%
1 102
11.5%
( 96
10.8%
) 96
10.8%
2 69
7.8%
0 63
 
7.1%
3 54
 
6.1%
6 43
 
4.9%
5 38
 
4.3%
4 37
 
4.2%
Other values (5) 129
14.6%
Latin
ValueCountFrequency (%)
e 2
18.2%
C 2
18.2%
A 2
18.2%
E 1
9.1%
t 1
9.1%
r 1
9.1%
n 1
9.1%
D 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 896
50.4%
Hangul 881
49.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
158
17.6%
1 102
11.4%
( 96
10.7%
) 96
10.7%
2 69
7.7%
0 63
 
7.0%
3 54
 
6.0%
6 43
 
4.8%
5 38
 
4.2%
4 37
 
4.1%
Other values (13) 140
15.6%
Hangul
ValueCountFrequency (%)
동 138
 
15.7%
로 95
 
10.8%
번 56
 
6.4%
길 53
 
6.0%
대 38
 
4.3%
감 33
 
3.7%
장 32
 
3.6%
전 31
 
3.5%
낙 27
 
3.1%
학 20
 
2.3%
Other values (69) 358
40.6%

연락처
Text

MISSING 

Distinct62
Distinct (%)74.7%
Missing12
Missing (%)12.6%
Memory size892.0 B
2024-04-06T17:35:01.304706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.048193
Min length12

Characters and Unicode

Total characters1000
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

Unique45 ?
Unique (%)54.2%

Sample

1st row051-317-0371
2nd row051-324-7288
3rd row051-327-2727
4th row051-710-1440
5th row051-301-1655
ValueCountFrequency (%)
051-315-5608 4
 
4.8%
051-314-3900 3
 
3.6%
051-327-1332 3
 
3.6%
051-303-8260 2
 
2.4%
070-7123-3337 2
 
2.4%
051-317-0371 2
 
2.4%
051-303-5234 2
 
2.4%
070-4202-7057 2
 
2.4%
051-746-0840 2
 
2.4%
051-301-8201 2
 
2.4%
Other values (52) 59
71.1%
2024-04-06T17:35:01.977429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 166
16.6%
0 162
16.2%
1 158
15.8%
3 131
13.1%
5 120
12.0%
2 66
 
6.6%
7 61
 
6.1%
4 47
 
4.7%
8 32
 
3.2%
6 29
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 834
83.4%
Dash Punctuation 166
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
19.4%
1 158
18.9%
3 131
15.7%
5 120
14.4%
2 66
7.9%
7 61
 
7.3%
4 47
 
5.6%
8 32
 
3.8%
6 29
 
3.5%
9 28
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 166
16.6%
0 162
16.2%
1 158
15.8%
3 131
13.1%
5 120
12.0%
2 66
 
6.6%
7 61
 
6.1%
4 47
 
4.7%
8 32
 
3.2%
6 29
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 166
16.6%
0 162
16.2%
1 158
15.8%
3 131
13.1%
5 120
12.0%
2 66
 
6.6%
7 61
 
6.1%
4 47
 
4.7%
8 32
 
3.2%
6 29
 
2.9%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
<NA>
91 
임시보관장소
 
4

Length

Max length6
Median length4
Mean length4.0842105
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 91
95.8%
임시보관장소 4
 
4.2%

Length

2024-04-06T17:35:02.235397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:35:02.428163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 91
95.8%
임시보관장소 4
 
4.2%

Interactions

2024-04-06T17:34:57.007620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:35:02.574291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종사업장명소재지연락처
연번1.0000.8770.0000.5970.487
업종0.8771.0000.0000.0000.000
사업장명0.0000.0001.0000.9990.999
소재지0.5970.0000.9991.0001.000
연락처0.4870.0000.9991.0001.000
2024-04-06T17:35:02.773956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고업종
비고1.0001.000
업종1.0001.000
2024-04-06T17:35:02.932138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종비고
연번1.0000.6231.000
업종0.6231.0001.000
비고1.0001.0001.000

Missing values

2024-04-06T17:34:57.191283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:34:57.397273image/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

연번업종사업장명소재지연락처비고
01수집운반(건설)(주)동아에너지낙동대로1062번길 17(감전동)051-317-0371<NA>
12수집운반(건설)(주)풍영(임시보관장소)장인로37번길 116(감전동)051-324-7288임시보관장소
23수집운반(건설)부산리사이클링(주)낙동대로901번길 25-18(감전동)051-327-2727<NA>
34수집운반(건설)호제환경산업(주)낙동대로 910, E315호(감전동, 마트월드)051-710-1440<NA>
45수집운반(건설)금강산개발(주)(임시보관장소)낙동대로1310번길 63(삼락동)051-301-1655임시보관장소
56수집운반(건설)(주)호생환경낙동대로 665(엄궁동)051-327-1332<NA>
67수집운반(건설)건설환경(주)하신번영로 498(엄궁동)051-316-4200<NA>
78수집운반(건설)에코포스트(주)낙동대로 671(엄궁동)051-317-3750<NA>
89수집운반(건설)(주)삼정환경산업하신번영로 462(엄궁동)051-313-0382<NA>
910수집운반(건설)(주)우리환경자원(임시보관장소)학장로 269(학장동)051-315-4404임시보관장소
연번업종사업장명소재지연락처비고
8586시설(압축시설)그린리사이클링-폐지새벽로45번길 70-14(학장동)070-7123-3337<NA>
8687시설(압축시설)(주)대흥리사이클링-폐지낙동대로 926(감전동)051-315-5608<NA>
8788시설(압축시설)한성산업(주)-폐금속류낙동대로915번길 12(감전동)051-324-7001<NA>
8889시설(압축시설)영조고철-고철사상로385번길 42 (덕포동)<NA><NA>
8990시설(순수발효방식 감량기)농협경제지주 부산공판장농산물시장로 25번길 75(엄궁동)051-314-4989<NA>
9091처리신고(수집운반)모란자원-폐축전지대동로239번길 13(학장동)051-316-7933<NA>
9192처리신고(수집운반)(주)대흥리사이클링-폐의류낙동대로 926(감전동)051-315-5608<NA>
9293처리신고(수집운반)그린리사이클링-폐의류새벽로45번길 70-14(학장동)070-7123-3337<NA>
9394처리신고(재활용)옥미산업-폐범퍼모덕로 16(삼락동)051-304-0335<NA>
9495처리신고(재활용)(주)동일케미칼-폐타이어장인로77번길 103-22(학장동)051-313-0657<NA>