Overview

Dataset statistics

Number of variables4
Number of observations89
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory33.5 B

Variable types

Text3
Categorical1

Dataset

Description부산광역시_북구_환경오염배출시설현황_20221128
Author부산광역시 북구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3069522

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:24:05.001708
Analysis finished2023-12-10 16:24:05.748650
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Text

UNIQUE 

Distinct89
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-12-11T01:24:05.953125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6741573
Min length4

Characters and Unicode

Total characters416
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)100.0%

Sample

1st row대기-1
2nd row대기-2
3rd row대기-3
4th row대기-4
5th row대기-5
ValueCountFrequency (%)
대기-1 1
 
1.1%
폐수-25 1
 
1.1%
폐수-45 1
 
1.1%
폐수-44 1
 
1.1%
폐수-43 1
 
1.1%
폐수-42 1
 
1.1%
폐수-41 1
 
1.1%
폐수-40 1
 
1.1%
폐수-39 1
 
1.1%
폐수-38 1
 
1.1%
Other values (79) 79
88.8%
2023-12-11T01:24:06.387135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 89
21.4%
폐 51
12.3%
수 51
12.3%
1 39
9.4%
2 20
 
4.8%
대 19
 
4.6%
3 19
 
4.6%
4 19
 
4.6%
기 19
 
4.6%
휘 17
 
4.1%
Other values (9) 73
17.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 178
42.8%
Decimal Number 149
35.8%
Dash Punctuation 89
21.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 39
26.2%
2 20
13.4%
3 19
12.8%
4 19
12.8%
5 11
 
7.4%
6 9
 
6.0%
7 9
 
6.0%
9 8
 
5.4%
8 8
 
5.4%
0 7
 
4.7%
Other Letter
ValueCountFrequency (%)
폐 51
28.7%
수 51
28.7%
대 19
 
10.7%
기 19
 
10.7%
휘 17
 
9.6%
발 17
 
9.6%
소 2
 
1.1%
음 2
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 238
57.2%
Hangul 178
42.8%

Most frequent character per script

Common
ValueCountFrequency (%)
- 89
37.4%
1 39
16.4%
2 20
 
8.4%
3 19
 
8.0%
4 19
 
8.0%
5 11
 
4.6%
6 9
 
3.8%
7 9
 
3.8%
9 8
 
3.4%
8 8
 
3.4%
Hangul
ValueCountFrequency (%)
폐 51
28.7%
수 51
28.7%
대 19
 
10.7%
기 19
 
10.7%
휘 17
 
9.6%
발 17
 
9.6%
소 2
 
1.1%
음 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238
57.2%
Hangul 178
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 89
37.4%
1 39
16.4%
2 20
 
8.4%
3 19
 
8.0%
4 19
 
8.0%
5 11
 
4.6%
6 9
 
3.8%
7 9
 
3.8%
9 8
 
3.4%
8 8
 
3.4%
Hangul
ValueCountFrequency (%)
폐 51
28.7%
수 51
28.7%
대 19
 
10.7%
기 19
 
10.7%
휘 17
 
9.6%
발 17
 
9.6%
소 2
 
1.1%
음 2
 
1.1%

업소명
Text

Distinct80
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-12-11T01:24:06.599858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length18
Mean length10.41573
Min length3

Characters and Unicode

Total characters927
Distinct characters194
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)79.8%

Sample

1st row남영목재
2nd row투투자동차정비공업사
3rd row덕천정비공업사
4th row㈜수정자동차정비
5th row삼정정비㈜
ValueCountFrequency (%)
인당의료재단 3
 
2.6%
의료법인 3
 
2.6%
남영목재 2
 
1.8%
재)한호기독교선교회 2
 
1.8%
성도주유소 2
 
1.8%
㈜삼보 2
 
1.8%
광신석유㈜백양대로주유소 2
 
1.8%
부민병원 2
 
1.8%
보건환경연구원 2
 
1.8%
sk에너지㈜신광주유소 2
 
1.8%
Other values (88) 92
80.7%
2023-12-11T01:24:06.940644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 38
 
4.1%
주 38
 
4.1%
유 36
 
3.9%
㈜ 33
 
3.6%
25
 
2.7%
부 20
 
2.2%
스 18
 
1.9%
산 16
 
1.7%
지 13
 
1.4%
에 12
 
1.3%
Other values (184) 678
73.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 814
87.8%
Other Symbol 33
 
3.6%
Space Separator 25
 
2.7%
Open Punctuation 12
 
1.3%
Close Punctuation 12
 
1.3%
Uppercase Letter 11
 
1.2%
Decimal Number 9
 
1.0%
Math Symbol 7
 
0.8%
Other Punctuation 2
 
0.2%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 38
 
4.7%
주 38
 
4.7%
유 36
 
4.4%
부 20
 
2.5%
스 18
 
2.2%
산 16
 
2.0%
지 13
 
1.6%
에 12
 
1.5%
구 12
 
1.5%
단 12
 
1.5%
Other values (165) 599
73.6%
Uppercase Letter
ValueCountFrequency (%)
S 4
36.4%
K 4
36.4%
N 1
 
9.1%
T 1
 
9.1%
G 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
2 5
55.6%
4 3
33.3%
1 1
 
11.1%
Math Symbol
ValueCountFrequency (%)
< 3
42.9%
> 3
42.9%
~ 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
/ 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
k 1
50.0%
Other Symbol
ValueCountFrequency (%)
㈜ 33
100.0%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 847
91.4%
Common 67
 
7.2%
Latin 13
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 38
 
4.5%
주 38
 
4.5%
유 36
 
4.3%
㈜ 33
 
3.9%
부 20
 
2.4%
스 18
 
2.1%
산 16
 
1.9%
지 13
 
1.5%
에 12
 
1.4%
구 12
 
1.4%
Other values (166) 611
72.1%
Common
ValueCountFrequency (%)
25
37.3%
( 12
17.9%
) 12
17.9%
2 5
 
7.5%
< 3
 
4.5%
> 3
 
4.5%
4 3
 
4.5%
1 1
 
1.5%
. 1
 
1.5%
~ 1
 
1.5%
Latin
ValueCountFrequency (%)
S 4
30.8%
K 4
30.8%
s 1
 
7.7%
k 1
 
7.7%
N 1
 
7.7%
T 1
 
7.7%
G 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 814
87.8%
ASCII 80
 
8.6%
None 33
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 38
 
4.7%
주 38
 
4.7%
유 36
 
4.4%
부 20
 
2.5%
스 18
 
2.2%
산 16
 
2.0%
지 13
 
1.6%
에 12
 
1.5%
구 12
 
1.5%
단 12
 
1.5%
Other values (165) 599
73.6%
None
ValueCountFrequency (%)
㈜ 33
100.0%
ASCII
ValueCountFrequency (%)
25
31.2%
( 12
15.0%
) 12
15.0%
2 5
 
6.2%
S 4
 
5.0%
K 4
 
5.0%
< 3
 
3.8%
> 3
 
3.8%
4 3
 
3.8%
1 1
 
1.2%
Other values (8) 8
 
10.0%

도로명주소
Text

Distinct75
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-12-11T01:24:07.270152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length13.325843
Min length9

Characters and Unicode

Total characters1186
Distinct characters57
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

Unique61 ?
Unique (%)68.5%

Sample

1st row사상로 606(구포동)
2nd row낙동대로 1751(구포동)
3rd row만덕대로 171-1(덕천동)
4th row금곡대로 100(덕천동)
5th row금곡대로37번길 15(덕천동 )
ValueCountFrequency (%)
금곡대로 25
 
13.3%
만덕대로 13
 
6.9%
사상로 7
 
3.7%
덕천로 6
 
3.2%
낙동대로 5
 
2.7%
시랑로 4
 
2.1%
37(덕천동 3
 
1.6%
효열로 3
 
1.6%
3
 
1.6%
백양대로 3
 
1.6%
Other values (94) 116
61.7%
2023-12-11T01:24:07.724565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
99
 
8.3%
동 96
 
8.1%
) 88
 
7.4%
( 88
 
7.4%
로 86
 
7.3%
덕 68
 
5.7%
대 51
 
4.3%
1 50
 
4.2%
천 41
 
3.5%
곡 38
 
3.2%
Other values (47) 481
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 631
53.2%
Decimal Number 273
23.0%
Space Separator 99
 
8.3%
Close Punctuation 88
 
7.4%
Open Punctuation 88
 
7.4%
Dash Punctuation 7
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 96
15.2%
로 86
13.6%
덕 68
10.8%
대 51
 
8.1%
천 41
 
6.5%
곡 38
 
6.0%
금 38
 
6.0%
만 28
 
4.4%
구 27
 
4.3%
포 25
 
4.0%
Other values (33) 133
21.1%
Decimal Number
ValueCountFrequency (%)
1 50
18.3%
6 31
11.4%
2 31
11.4%
5 31
11.4%
3 27
9.9%
7 25
9.2%
4 23
8.4%
0 20
 
7.3%
9 20
 
7.3%
8 15
 
5.5%
Space Separator
ValueCountFrequency (%)
99
100.0%
Close Punctuation
ValueCountFrequency (%)
) 88
100.0%
Open Punctuation
ValueCountFrequency (%)
( 88
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 631
53.2%
Common 555
46.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 96
15.2%
로 86
13.6%
덕 68
10.8%
대 51
 
8.1%
천 41
 
6.5%
곡 38
 
6.0%
금 38
 
6.0%
만 28
 
4.4%
구 27
 
4.3%
포 25
 
4.0%
Other values (33) 133
21.1%
Common
ValueCountFrequency (%)
99
17.8%
) 88
15.9%
( 88
15.9%
1 50
9.0%
6 31
 
5.6%
2 31
 
5.6%
5 31
 
5.6%
3 27
 
4.9%
7 25
 
4.5%
4 23
 
4.1%
Other values (4) 62
11.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 631
53.2%
ASCII 555
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
99
17.8%
) 88
15.9%
( 88
15.9%
1 50
9.0%
6 31
 
5.6%
2 31
 
5.6%
5 31
 
5.6%
3 27
 
4.9%
7 25
 
4.5%
4 23
 
4.1%
Other values (4) 62
11.2%
Hangul
ValueCountFrequency (%)
동 96
15.2%
로 86
13.6%
덕 68
10.8%
대 51
 
8.1%
천 41
 
6.5%
곡 38
 
6.0%
금 38
 
6.0%
만 28
 
4.4%
구 27
 
4.3%
포 25
 
4.0%
Other values (33) 133
21.1%

업종
Categorical

Distinct19
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size844.0 B
세차
37 
주유소
17 
병원
8 
정비
6 
주택관리
4 
Other values (14)
17 

Length

Max length10
Median length2
Mean length2.6179775
Min length2

Unique

Unique11 ?
Unique (%)12.4%

Sample

1st row목재
2nd row정비
3rd row정비
4th row정비
5th row정비

Common Values

ValueCountFrequency (%)
세차 37
41.6%
주유소 17
19.1%
병원 8
 
9.0%
정비 6
 
6.7%
주택관리 4
 
4.5%
실험실 2
 
2.2%
목재 2
 
2.2%
부동산임대업 2
 
2.2%
종합병원 1
 
1.1%
터널 1
 
1.1%
Other values (9) 9
 
10.1%

Length

2023-12-11T01:24:07.912280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
세차 37
41.6%
주유소 17
19.1%
병원 8
 
9.0%
정비 6
 
6.7%
주택관리 4
 
4.5%
실험실 2
 
2.2%
목재 2
 
2.2%
부동산임대업 2
 
2.2%
교육서비스업 1
 
1.1%
임대 1
 
1.1%
Other values (9) 9
 
10.1%

Correlations

2023-12-11T01:24:08.026748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업소명도로명주소업종
연번1.0001.0001.0001.000
업소명1.0001.0001.0000.982
도로명주소1.0001.0001.0000.986
업종1.0000.9820.9861.000

Missing values

2023-12-11T01:24:05.398624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:24:05.721230image/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

연번업소명도로명주소업종
0대기-1남영목재사상로 606(구포동)목재
1대기-2투투자동차정비공업사낙동대로 1751(구포동)정비
2대기-3덕천정비공업사만덕대로 171-1(덕천동)정비
3대기-4㈜수정자동차정비금곡대로 100(덕천동)정비
4대기-5삼정정비㈜금곡대로37번길 15(덕천동 )정비
5대기-6(주)엘리트종합정비낙동대로 1755(구포동)정비
6대기-7의료법인 인당의료재단 부민병원만덕대로 59(덕천동)종합병원
7대기-8현대스포렉스덕천로259번길 5(만덕동)종합스포츠시설운영업
8대기-9화명롯데캐슬카이저입주자대표회의금곡대로 166(화명동)부동산임대업
9대기-10구포 국일정비구포만세길 2(구포동)정비
연번업소명도로명주소업종
79휘발-8경덕주유소시랑로 58(구포동)주유소
80휘발-9세일석유(주)금곡주유소금곡대로 604(금곡동)주유소
81휘발-10북부산새마을금고금곡대로 37(덕천동)주유소
82휘발-11지에스칼텍스(주)라인주유소덕천로 179(만덕동)주유소
83휘발-12광신석유㈜덕천고속주유소의성로 20(덕천동)주유소
84휘발-13화명신도시주유소금곡대로 203(화명동)주유소
85휘발-14SK에너지㈜신광주유소덕천로 275(만덕동)주유소
86휘발-15부경에너지㈜만복드림주유소만덕대로 170(덕천동)주유소
87휘발-16동양가스산업㈜명품주유소금곡대로 113(덕천동)주유소
88휘발-17성도주유소낙동대로 1642(구포동)주유소