Overview

Dataset statistics

Number of variables3
Number of observations308
Missing cells26
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory24.4 B

Variable types

Text3

Dataset

Description서울특별시 용산구 쓰레기상습무단투기지역현황(연번, 도로명주소, 상세위치)에 대한 정보를 제공합니다. 해당지역은 경험적으로 자주 단속하는 지역이며 관리데이터가 아님을 안내드립니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15106964/fileData.do

Alerts

상세위치 has 26 (8.4%) missing valuesMissing
연번 has unique valuesUnique
도로명주소 has unique valuesUnique

Reproduction

Analysis started2024-04-18 02:00:21.766730
Analysis finished2024-04-18 02:00:23.075077
Duration1.31 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Text

UNIQUE 

Distinct308
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-04-18T11:00:23.291291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length6.2402597
Min length5

Characters and Unicode

Total characters1922
Distinct characters37
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

Unique308 ?
Unique (%)100.0%

Sample

1st row후암동-1
2nd row후암동-2
3rd row후암동-3
4th row후암동-4
5th row후암동-5
ValueCountFrequency (%)
후암동-1 1
 
0.3%
이태원제2동-5 1
 
0.3%
한남동-4 1
 
0.3%
한남동-3 1
 
0.3%
한남동-2 1
 
0.3%
한남동-1 1
 
0.3%
이태원제2동-9 1
 
0.3%
이태원제2동-8 1
 
0.3%
이태원제2동-7 1
 
0.3%
이태원1동-7 1
 
0.3%
Other values (298) 298
96.8%
2024-04-18T11:00:23.662002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 308
16.0%
- 308
16.0%
1 169
 
8.8%
2 143
 
7.4%
한 67
 
3.5%
남 66
 
3.4%
3 59
 
3.1%
원 50
 
2.6%
광 40
 
2.1%
보 40
 
2.1%
Other values (27) 672
35.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1038
54.0%
Decimal Number 576
30.0%
Dash Punctuation 308
 
16.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 308
29.7%
한 67
 
6.5%
남 66
 
6.4%
원 50
 
4.8%
광 40
 
3.9%
보 40
 
3.9%
효 37
 
3.6%
로 36
 
3.5%
용 35
 
3.4%
청 33
 
3.2%
Other values (16) 326
31.4%
Decimal Number
ValueCountFrequency (%)
1 169
29.3%
2 143
24.8%
3 59
 
10.2%
4 37
 
6.4%
5 32
 
5.6%
7 29
 
5.0%
6 29
 
5.0%
9 27
 
4.7%
8 27
 
4.7%
0 24
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1038
54.0%
Common 884
46.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 308
29.7%
한 67
 
6.5%
남 66
 
6.4%
원 50
 
4.8%
광 40
 
3.9%
보 40
 
3.9%
효 37
 
3.6%
로 36
 
3.5%
용 35
 
3.4%
청 33
 
3.2%
Other values (16) 326
31.4%
Common
ValueCountFrequency (%)
- 308
34.8%
1 169
19.1%
2 143
16.2%
3 59
 
6.7%
4 37
 
4.2%
5 32
 
3.6%
7 29
 
3.3%
6 29
 
3.3%
9 27
 
3.1%
8 27
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1038
54.0%
ASCII 884
46.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 308
29.7%
한 67
 
6.5%
남 66
 
6.4%
원 50
 
4.8%
광 40
 
3.9%
보 40
 
3.9%
효 37
 
3.6%
로 36
 
3.5%
용 35
 
3.4%
청 33
 
3.2%
Other values (16) 326
31.4%
ASCII
ValueCountFrequency (%)
- 308
34.8%
1 169
19.1%
2 143
16.2%
3 59
 
6.7%
4 37
 
4.2%
5 32
 
3.6%
7 29
 
3.3%
6 29
 
3.3%
9 27
 
3.1%
8 27
 
3.1%

도로명주소
Text

UNIQUE 

Distinct308
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-04-18T11:00:23.853518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length25.5
Mean length13.487013
Min length5

Characters and Unicode

Total characters4154
Distinct characters218
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)100.0%

Sample

1st row두텁바위로1길107
2nd row후암로13가길22
3rd row후암로28길 57
4th row한강대로104길 67
5th row후암로4길25
ValueCountFrequency (%)
앞 44
 
5.1%
옆 19
 
2.2%
용산구 11
 
1.3%
맞은편 11
 
1.3%
우사단로10길 10
 
1.2%
한강대로 8
 
0.9%
우사단로4길 7
 
0.8%
보광로 7
 
0.8%
14 7
 
0.8%
전주 7
 
0.8%
Other values (484) 737
84.9%
2024-04-18T11:00:24.152536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
569
 
13.7%
로 310
 
7.5%
1 265
 
6.4%
길 247
 
5.9%
2 157
 
3.8%
4 153
 
3.7%
3 127
 
3.1%
5 112
 
2.7%
7 101
 
2.4%
0 94
 
2.3%
Other values (208) 2019
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2182
52.5%
Decimal Number 1249
30.1%
Space Separator 569
 
13.7%
Dash Punctuation 74
 
1.8%
Open Punctuation 30
 
0.7%
Close Punctuation 30
 
0.7%
Other Punctuation 11
 
0.3%
Uppercase Letter 5
 
0.1%
Lowercase Letter 3
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 310
 
14.2%
길 247
 
11.3%
대 82
 
3.8%
사 61
 
2.8%
원 53
 
2.4%
동 50
 
2.3%
앞 49
 
2.2%
한 46
 
2.1%
강 41
 
1.9%
우 41
 
1.9%
Other values (185) 1202
55.1%
Decimal Number
ValueCountFrequency (%)
1 265
21.2%
2 157
12.6%
4 153
12.2%
3 127
10.2%
5 112
9.0%
7 101
 
8.1%
0 94
 
7.5%
9 94
 
7.5%
6 87
 
7.0%
8 59
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
S 2
40.0%
L 1
20.0%
A 1
20.0%
G 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
t 1
33.3%
p 1
33.3%
a 1
33.3%
Space Separator
ValueCountFrequency (%)
569
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2182
52.5%
Common 1964
47.3%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 310
 
14.2%
길 247
 
11.3%
대 82
 
3.8%
사 61
 
2.8%
원 53
 
2.4%
동 50
 
2.3%
앞 49
 
2.2%
한 46
 
2.1%
강 41
 
1.9%
우 41
 
1.9%
Other values (185) 1202
55.1%
Common
ValueCountFrequency (%)
569
29.0%
1 265
13.5%
2 157
 
8.0%
4 153
 
7.8%
3 127
 
6.5%
5 112
 
5.7%
7 101
 
5.1%
0 94
 
4.8%
9 94
 
4.8%
6 87
 
4.4%
Other values (6) 205
 
10.4%
Latin
ValueCountFrequency (%)
S 2
25.0%
L 1
12.5%
A 1
12.5%
t 1
12.5%
p 1
12.5%
G 1
12.5%
a 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2182
52.5%
ASCII 1972
47.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
569
28.9%
1 265
13.4%
2 157
 
8.0%
4 153
 
7.8%
3 127
 
6.4%
5 112
 
5.7%
7 101
 
5.1%
0 94
 
4.8%
9 94
 
4.8%
6 87
 
4.4%
Other values (13) 213
 
10.8%
Hangul
ValueCountFrequency (%)
로 310
 
14.2%
길 247
 
11.3%
대 82
 
3.8%
사 61
 
2.8%
원 53
 
2.4%
동 50
 
2.3%
앞 49
 
2.2%
한 46
 
2.1%
강 41
 
1.9%
우 41
 
1.9%
Other values (185) 1202
55.1%

상세위치
Text

MISSING 

Distinct155
Distinct (%)55.0%
Missing26
Missing (%)8.4%
Memory size2.5 KiB
2024-04-18T11:00:24.325740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length24
Mean length12.48227
Min length2

Characters and Unicode

Total characters3520
Distinct characters190
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

Unique113 ?
Unique (%)40.1%

Sample

1st row행남팰리스 앞 상업지역 및 골목길 4거리
2nd row백합빌라 앞 도로변 및 쓰레기 수거지역
3rd row에지앙 담장 전신주 옆
4th row후암시장(구 풍년순댓국 앞) 골목4거리
5th row지월장 옆 전신주
ValueCountFrequency (%)
거점지역 66
 
7.1%
쓰레기 64
 
6.9%
수거 60
 
6.4%
옆 55
 
5.9%
전신주옆 49
 
5.3%
등 43
 
4.6%
전신주 39
 
4.2%
앞 34
 
3.6%
밀집지역 28
 
3.0%
주거밀집지역 27
 
2.9%
Other values (153) 467
50.1%
2024-04-18T11:00:24.627625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
668
19.0%
지 192
 
5.5%
거 186
 
5.3%
주 183
 
5.2%
역 169
 
4.8%
, 145
 
4.1%
옆 106
 
3.0%
전 101
 
2.9%
신 89
 
2.5%
기 86
 
2.4%
Other values (180) 1595
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2674
76.0%
Space Separator 668
 
19.0%
Other Punctuation 146
 
4.1%
Decimal Number 26
 
0.7%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 192
 
7.2%
거 186
 
7.0%
주 183
 
6.8%
역 169
 
6.3%
옆 106
 
4.0%
전 101
 
3.8%
신 89
 
3.3%
기 86
 
3.2%
밀 83
 
3.1%
집 83
 
3.1%
Other values (166) 1396
52.2%
Decimal Number
ValueCountFrequency (%)
4 7
26.9%
5 4
15.4%
2 3
11.5%
3 3
11.5%
1 3
11.5%
7 2
 
7.7%
9 2
 
7.7%
6 1
 
3.8%
0 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 145
99.3%
? 1
 
0.7%
Space Separator
ValueCountFrequency (%)
668
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2674
76.0%
Common 846
 
24.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 192
 
7.2%
거 186
 
7.0%
주 183
 
6.8%
역 169
 
6.3%
옆 106
 
4.0%
전 101
 
3.8%
신 89
 
3.3%
기 86
 
3.2%
밀 83
 
3.1%
집 83
 
3.1%
Other values (166) 1396
52.2%
Common
ValueCountFrequency (%)
668
79.0%
, 145
 
17.1%
4 7
 
0.8%
5 4
 
0.5%
) 3
 
0.4%
2 3
 
0.4%
3 3
 
0.4%
( 3
 
0.4%
1 3
 
0.4%
7 2
 
0.2%
Other values (4) 5
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2674
76.0%
ASCII 846
 
24.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
668
79.0%
, 145
 
17.1%
4 7
 
0.8%
5 4
 
0.5%
) 3
 
0.4%
2 3
 
0.4%
3 3
 
0.4%
( 3
 
0.4%
1 3
 
0.4%
7 2
 
0.2%
Other values (4) 5
 
0.6%
Hangul
ValueCountFrequency (%)
지 192
 
7.2%
거 186
 
7.0%
주 183
 
6.8%
역 169
 
6.3%
옆 106
 
4.0%
전 101
 
3.8%
신 89
 
3.3%
기 86
 
3.2%
밀 83
 
3.1%
집 83
 
3.1%
Other values (166) 1396
52.2%

Missing values

2024-04-18T11:00:23.047719image/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

연번도로명주소상세위치
0후암동-1두텁바위로1길107행남팰리스 앞 상업지역 및 골목길 4거리
1후암동-2후암로13가길22백합빌라 앞 도로변 및 쓰레기 수거지역
2후암동-3후암로28길 57에지앙 담장 전신주 옆
3후암동-4한강대로104길 67후암시장(구 풍년순댓국 앞) 골목4거리
4후암동-5후암로4길25지월장 옆 전신주
5후암동-6후암로40길3거주자주차구역 전신주 옆 ?
6후암동-7후암로34길 11파크빌 앞 전신주 옆
7후암동-8후암로16길 15용산더힐 빌라 앞 전신주 옆
8후암동-9두텁바위로37길 13골목길 삼거리 전신주
9후암동-10소월로2나길 19좁은골목길 전신주 옆
연번도로명주소상세위치
298보광동-31보광로 30가길 1919 맞은편
299보광동-32장문로 45라길 5담장 옆
300보광동-33보광로 24길 924길 9 맞은편
301보광동-34우사단로 6길 14담장 옆
302보광동-35장문로 45가길 3345가길 33 맞은편 전신주 옆
303보광동-36장문로 45가길 2745가길 27 맞은편
304보광동-37보광로 7길 9경로당 맞은편
305보광동-38장문로 45바길 40옆 담장 전신주
306보광동-39장문로 45바길 1145바길 11 맞은편 담장
307보광동-40장문로 45바길 745바길 7 건물입구 앞