Overview

Dataset statistics

Number of variables5
Number of observations187
Missing cells126
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory40.7 B

Variable types

Categorical1
Text3
DateTime1

Dataset

Description부산광역시 중구 관내 쓰레기종량제봉투 판매업소 현황에 대한 데이터로 행정동, 상호명, 소재지, 연락처, 지정일자의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15028922/fileData.do

Alerts

연락처 has 63 (33.7%) missing valuesMissing
지정일자 has 63 (33.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 20:34:54.610458
Analysis finished2023-12-12 20:34:55.104183
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Categorical

Distinct10
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
중앙동
40 
부평동
28 
남포동
28 
보수동
24 
대청동
17 
Other values (5)
50 

Length

Max length4
Median length3
Mean length3.0909091
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
중앙동 40
21.4%
부평동 28
15.0%
남포동 28
15.0%
보수동 24
12.8%
대청동 17
9.1%
영주2동 16
 
8.6%
동광동 11
 
5.9%
광복 11
 
5.9%
영주1동 11
 
5.9%
보수동 1
 
0.5%

Length

2023-12-13T05:34:55.203609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:34:55.381504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중앙동 40
21.4%
부평동 28
15.0%
남포동 28
15.0%
보수동 25
13.4%
대청동 17
9.1%
영주2동 16
 
8.6%
동광동 11
 
5.9%
광복 11
 
5.9%
영주1동 11
 
5.9%

상호명
Text

Distinct181
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T05:34:55.695407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length7.7112299
Min length2

Characters and Unicode

Total characters1442
Distinct characters204
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)94.1%

Sample

1st row은하
2nd row만물상회
3rd row빅세일마트
4th rowk할인마트
5th row후레쉬마트
ValueCountFrequency (%)
세븐일레븐 18
 
6.8%
gs25 11
 
4.2%
씨유 9
 
3.4%
이마트24 5
 
1.9%
미니스톱 4
 
1.5%
cu 4
 
1.5%
지에스(gs)25 3
 
1.1%
만물상회 3
 
1.1%
중앙점 2
 
0.8%
자갈치점 2
 
0.8%
Other values (192) 203
76.9%
2023-12-13T05:34:56.158082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 93
 
6.4%
77
 
5.3%
부 60
 
4.2%
산 50
 
3.5%
마 45
 
3.1%
트 43
 
3.0%
2 41
 
2.8%
븐 40
 
2.8%
일 32
 
2.2%
퍼 31
 
2.1%
Other values (194) 930
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1190
82.5%
Decimal Number 87
 
6.0%
Space Separator 77
 
5.3%
Uppercase Letter 70
 
4.9%
Open Punctuation 5
 
0.3%
Close Punctuation 5
 
0.3%
Other Symbol 4
 
0.3%
Lowercase Letter 3
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 93
 
7.8%
부 60
 
5.0%
산 50
 
4.2%
마 45
 
3.8%
트 43
 
3.6%
븐 40
 
3.4%
일 32
 
2.7%
퍼 31
 
2.6%
중 31
 
2.6%
슈 28
 
2.4%
Other values (174) 737
61.9%
Decimal Number
ValueCountFrequency (%)
2 41
47.1%
5 30
34.5%
4 11
 
12.6%
1 2
 
2.3%
0 1
 
1.1%
3 1
 
1.1%
6 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
S 26
37.1%
G 24
34.3%
C 10
 
14.3%
U 9
 
12.9%
A 1
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
k 1
33.3%
s 1
33.3%
g 1
33.3%
Space Separator
ValueCountFrequency (%)
77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1194
82.8%
Common 175
 
12.1%
Latin 73
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 93
 
7.8%
부 60
 
5.0%
산 50
 
4.2%
마 45
 
3.8%
트 43
 
3.6%
븐 40
 
3.4%
일 32
 
2.7%
퍼 31
 
2.6%
중 31
 
2.6%
슈 28
 
2.3%
Other values (175) 741
62.1%
Common
ValueCountFrequency (%)
77
44.0%
2 41
23.4%
5 30
 
17.1%
4 11
 
6.3%
( 5
 
2.9%
) 5
 
2.9%
1 2
 
1.1%
. 1
 
0.6%
0 1
 
0.6%
3 1
 
0.6%
Latin
ValueCountFrequency (%)
S 26
35.6%
G 24
32.9%
C 10
 
13.7%
U 9
 
12.3%
A 1
 
1.4%
k 1
 
1.4%
s 1
 
1.4%
g 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1190
82.5%
ASCII 248
 
17.2%
None 4
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 93
 
7.8%
부 60
 
5.0%
산 50
 
4.2%
마 45
 
3.8%
트 43
 
3.6%
븐 40
 
3.4%
일 32
 
2.7%
퍼 31
 
2.6%
중 31
 
2.6%
슈 28
 
2.4%
Other values (174) 737
61.9%
ASCII
ValueCountFrequency (%)
77
31.0%
2 41
16.5%
5 30
 
12.1%
S 26
 
10.5%
G 24
 
9.7%
4 11
 
4.4%
C 10
 
4.0%
U 9
 
3.6%
( 5
 
2.0%
) 5
 
2.0%
Other values (9) 10
 
4.0%
None
ValueCountFrequency (%)
㈜ 4
100.0%

소재지
Text

Distinct182
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T05:34:56.471226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length33
Mean length19.786096
Min length14

Characters and Unicode

Total characters3700
Distinct characters90
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

Unique177 ?
Unique (%)94.7%

Sample

1st row부산광역시 중구 중앙대로 21, 107호
2nd row부산광역시 중구 대청로138번길 11
3rd row부산광역시 중구 중앙대로116번길 7
4th row부산광역시 중구 해관로 41
5th row부산광역시 중구 충장대로9번길 30
ValueCountFrequency (%)
부산광역시 187
23.4%
중구 187
23.4%
중앙대로 11
 
1.4%
보수1가 10
 
1.3%
1층 9
 
1.1%
대청로 8
 
1.0%
구덕로 7
 
0.9%
14 6
 
0.8%
망양로 6
 
0.8%
자갈치로 6
 
0.8%
Other values (240) 361
45.2%
2023-12-13T05:34:57.009872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
615
16.6%
중 230
 
6.2%
광 208
 
5.6%
부 206
 
5.6%
구 206
 
5.6%
1 201
 
5.4%
산 191
 
5.2%
시 189
 
5.1%
역 187
 
5.1%
로 126
 
3.4%
Other values (80) 1341
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2252
60.9%
Decimal Number 682
 
18.4%
Space Separator 615
 
16.6%
Dash Punctuation 54
 
1.5%
Close Punctuation 32
 
0.9%
Open Punctuation 32
 
0.9%
Other Punctuation 32
 
0.9%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 230
10.2%
광 208
 
9.2%
부 206
 
9.1%
구 206
 
9.1%
산 191
 
8.5%
시 189
 
8.4%
역 187
 
8.3%
로 126
 
5.6%
가 73
 
3.2%
길 69
 
3.1%
Other values (64) 567
25.2%
Decimal Number
ValueCountFrequency (%)
1 201
29.5%
2 99
14.5%
4 76
 
11.1%
3 75
 
11.0%
5 49
 
7.2%
9 42
 
6.2%
7 40
 
5.9%
0 36
 
5.3%
6 35
 
5.1%
8 29
 
4.3%
Space Separator
ValueCountFrequency (%)
615
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Other Punctuation
ValueCountFrequency (%)
, 32
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2252
60.9%
Common 1447
39.1%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 230
10.2%
광 208
 
9.2%
부 206
 
9.1%
구 206
 
9.1%
산 191
 
8.5%
시 189
 
8.4%
역 187
 
8.3%
로 126
 
5.6%
가 73
 
3.2%
길 69
 
3.1%
Other values (64) 567
25.2%
Common
ValueCountFrequency (%)
615
42.5%
1 201
 
13.9%
2 99
 
6.8%
4 76
 
5.3%
3 75
 
5.2%
- 54
 
3.7%
5 49
 
3.4%
9 42
 
2.9%
7 40
 
2.8%
0 36
 
2.5%
Other values (5) 160
 
11.1%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2252
60.9%
ASCII 1448
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
615
42.5%
1 201
 
13.9%
2 99
 
6.8%
4 76
 
5.2%
3 75
 
5.2%
- 54
 
3.7%
5 49
 
3.4%
9 42
 
2.9%
7 40
 
2.8%
0 36
 
2.5%
Other values (6) 161
 
11.1%
Hangul
ValueCountFrequency (%)
중 230
10.2%
광 208
 
9.2%
부 206
 
9.1%
구 206
 
9.1%
산 191
 
8.5%
시 189
 
8.4%
역 187
 
8.3%
로 126
 
5.6%
가 73
 
3.2%
길 69
 
3.1%
Other values (64) 567
25.2%

연락처
Text

MISSING 

Distinct123
Distinct (%)99.2%
Missing63
Missing (%)33.7%
Memory size1.6 KiB
2023-12-13T05:34:57.304539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1488
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

Unique122 ?
Unique (%)98.4%

Sample

1st row051-441-0050
2nd row051-245-1250
3rd row051-242-2912
4th row051-463-6518
5th row051-441-9594
ValueCountFrequency (%)
051-241-5175 2
 
1.6%
051-245-8266 1
 
0.8%
051-441-0050 1
 
0.8%
051-245-6377 1
 
0.8%
051-246-8764 1
 
0.8%
051-246-9329 1
 
0.8%
051-246-6795 1
 
0.8%
051-245-0834 1
 
0.8%
051-231-0698 1
 
0.8%
051-242-1577 1
 
0.8%
Other values (113) 113
91.1%
2023-12-13T05:34:57.719590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 248
16.7%
5 215
14.4%
1 187
12.6%
4 172
11.6%
0 166
11.2%
2 154
10.3%
6 114
7.7%
3 70
 
4.7%
8 59
 
4.0%
7 57
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1240
83.3%
Dash Punctuation 248
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 215
17.3%
1 187
15.1%
4 172
13.9%
0 166
13.4%
2 154
12.4%
6 114
9.2%
3 70
 
5.6%
8 59
 
4.8%
7 57
 
4.6%
9 46
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1488
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 248
16.7%
5 215
14.4%
1 187
12.6%
4 172
11.6%
0 166
11.2%
2 154
10.3%
6 114
7.7%
3 70
 
4.7%
8 59
 
4.0%
7 57
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 248
16.7%
5 215
14.4%
1 187
12.6%
4 172
11.6%
0 166
11.2%
2 154
10.3%
6 114
7.7%
3 70
 
4.7%
8 59
 
4.0%
7 57
 
3.8%

지정일자
Date

MISSING 

Distinct115
Distinct (%)92.7%
Missing63
Missing (%)33.7%
Memory size1.6 KiB
Minimum1994-12-21 00:00:00
Maximum2023-02-14 00:00:00
2023-12-13T05:34:57.909139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:58.081574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Missing values

2023-12-13T05:34:54.867808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:34:54.958527image/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.
2023-12-13T05:34:55.050590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

행정동상호명소재지연락처지정일자
0중앙동은하부산광역시 중구 중앙대로 21, 107호051-441-00502015-04-03
1중앙동만물상회부산광역시 중구 대청로138번길 11051-245-12502009-12-07
2중앙동빅세일마트부산광역시 중구 중앙대로116번길 7<NA>2013-10-01
3중앙동k할인마트부산광역시 중구 해관로 41051-242-29122012-10-30
4중앙동후레쉬마트부산광역시 중구 충장대로9번길 30051-463-65182013-09-11
5중앙동일신수퍼부산광역시 중구 중앙대로116번길 12051-441-9594<NA>
6중앙동중앙전기철물. 소품부산광역시 중구 40계단길 10051-464-34392021-03-30
7중앙동플러스할인마트부산광역시 중구 중앙동2가 89051-464-41122013-12-16
8중앙동한진상회부산광역시 중구 충장대로5번길 69-1051-462-25472013-10-08
9중앙동GS25 중부산점부산광역시 중구 중앙대로 31<NA>2016-02-22
행정동상호명소재지연락처지정일자
177영주2동탑세일마트부산광역시 중구 영주로 52051-462-00322013-03-26
178영주2동GS25민주전망대점부산광역시 중구 망양로 459051-468-6736<NA>
179영주2동동주슈퍼부산광역시 중구 망양로 405051-265-6804<NA>
180영주2동GS25부산민주점부산광역시 중구 망양로358번길 1051-462-96452018-03-20
181영주2동함안상회부산광역시 중구 망양로 369051-469-4710<NA>
182영주2동GS25동아햇살점부산광역시 중구 영주로 73051-466-5943<NA>
183영주2동금호빅마트부산광역시 중구 영주로 49051-467-65702018-03-23
184영주2동삼거리상회부산광역시 중구 동영로 38051-463-3321<NA>
185영주2동GS25부산터널점부산광역시 중구 동영로64번길 21<NA>2019-03-19
186영주2동행복플러스 25부산광역시 중구 동영로 38-1<NA>2020-10-19