Overview

Dataset statistics

Number of variables7
Number of observations114
Missing cells177
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory57.2 B

Variable types

Categorical1
Text3
Boolean3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-20453/F/1/datasetView.do

Alerts

일반쓰레기,음식물쓰레기 has constant value ""Constant
재활용 has constant value ""Constant
대형폐기물 has constant value ""Constant
재활용 has 22 (19.3%) missing valuesMissing
대형폐기물 has 45 (39.5%) missing valuesMissing
비고 has 110 (96.5%) missing valuesMissing
수거지역 has unique valuesUnique

Reproduction

Analysis started2023-12-11 04:11:22.066231
Analysis finished2023-12-11 04:11:23.045461
Duration0.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구
Categorical

Distinct25
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
송파구
9 
강남구
 
7
관악구
 
7
양천구
 
6
중구
 
6
Other values (20)
79 

Length

Max length3
Median length3
Mean length2.9473684
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row종로구
3rd row종로구
4th row중구
5th row중구

Common Values

ValueCountFrequency (%)
송파구 9
 
7.9%
강남구 7
 
6.1%
관악구 7
 
6.1%
양천구 6
 
5.3%
중구 6
 
5.3%
영등포 6
 
5.3%
강동구 5
 
4.4%
강서구 5
 
4.4%
동작구 5
 
4.4%
서초구 5
 
4.4%
Other values (15) 53
46.5%

Length

2023-12-11T13:11:23.203265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 9
 
7.9%
관악구 7
 
6.1%
강남구 7
 
6.1%
양천구 6
 
5.3%
중구 6
 
5.3%
영등포 6
 
5.3%
강동구 5
 
4.4%
강서구 5
 
4.4%
동작구 5
 
4.4%
서초구 5
 
4.4%
Other values (15) 53
46.5%

대행업체
Text

Distinct113
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-11T13:11:23.579436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.4385965
Min length2

Characters and Unicode

Total characters620
Distinct characters124
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112 ?
Unique (%)98.2%

Sample

1st row심창기업㈜
2nd row대승기업㈜
3rd row평아실업㈜
4th row거구실업
5th row동보환경
ValueCountFrequency (%)
나라환경산업 2
 
1.8%
심창기업㈜ 1
 
0.9%
동진환경(주 1
 
0.9%
삼지크린 1
 
0.9%
수정환경 1
 
0.9%
신봉그린 1
 
0.9%
신세계환경㈜ 1
 
0.9%
늘푸른환경㈜ 1
 
0.9%
남지환경㈜ 1
 
0.9%
기성환경㈜ 1
 
0.9%
Other values (103) 103
90.4%
2023-12-11T13:11:24.167464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 60
 
9.7%
환 51
 
8.2%
㈜ 42
 
6.8%
업 34
 
5.5%
( 31
 
5.0%
주 31
 
5.0%
) 31
 
5.0%
기 19
 
3.1%
산 11
 
1.8%
한 10
 
1.6%
Other values (114) 300
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 513
82.7%
Other Symbol 42
 
6.8%
Open Punctuation 31
 
5.0%
Close Punctuation 31
 
5.0%
Uppercase Letter 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 60
 
11.7%
환 51
 
9.9%
업 34
 
6.6%
주 31
 
6.0%
기 19
 
3.7%
산 11
 
2.1%
한 10
 
1.9%
용 10
 
1.9%
린 10
 
1.9%
성 9
 
1.8%
Other values (108) 268
52.2%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
D 1
33.3%
E 1
33.3%
Other Symbol
ValueCountFrequency (%)
㈜ 42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 555
89.5%
Common 62
 
10.0%
Latin 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 60
 
10.8%
환 51
 
9.2%
㈜ 42
 
7.6%
업 34
 
6.1%
주 31
 
5.6%
기 19
 
3.4%
산 11
 
2.0%
한 10
 
1.8%
용 10
 
1.8%
린 10
 
1.8%
Other values (109) 277
49.9%
Latin
ValueCountFrequency (%)
I 1
33.3%
D 1
33.3%
E 1
33.3%
Common
ValueCountFrequency (%)
( 31
50.0%
) 31
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 513
82.7%
ASCII 65
 
10.5%
None 42
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 60
 
11.7%
환 51
 
9.9%
업 34
 
6.6%
주 31
 
6.0%
기 19
 
3.7%
산 11
 
2.1%
한 10
 
1.9%
용 10
 
1.9%
린 10
 
1.9%
성 9
 
1.8%
Other values (108) 268
52.2%
None
ValueCountFrequency (%)
㈜ 42
100.0%
ASCII
ValueCountFrequency (%)
( 31
47.7%
) 31
47.7%
I 1
 
1.5%
D 1
 
1.5%
E 1
 
1.5%

일반쓰레기,음식물쓰레기
Boolean

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size246.0 B
True
114 
ValueCountFrequency (%)
True 114
100.0%
2023-12-11T13:11:24.313175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

재활용
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.1%
Missing22
Missing (%)19.3%
Memory size360.0 B
True
92 
(Missing)
22 
ValueCountFrequency (%)
True 92
80.7%
(Missing) 22
 
19.3%
2023-12-11T13:11:24.409236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

대형폐기물
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.4%
Missing45
Missing (%)39.5%
Memory size360.0 B
True
69 
(Missing)
45 
ValueCountFrequency (%)
True 69
60.5%
(Missing) 45
39.5%
2023-12-11T13:11:24.519576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

수거지역
Text

UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-11T13:11:24.739950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length33
Mean length17.535088
Min length3

Characters and Unicode

Total characters1999
Distinct characters201
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

Unique114 ?
Unique (%)100.0%

Sample

1st row청운효자동,사직동,삼청동,가회동,부암동,평창동,무악동,교남동,혜화동
2nd row종로1가동,종로2가동,종로3가동,종로4가동,연지동
3rd row종로5가동,6가동,이화동,창신1동,창신2동,창신3동,숭인1동,숭인2동
4th row명동 일부
5th row약수동,신당5동,동화동,황학동
ValueCountFrequency (%)
일부 5
 
3.8%
명동 2
 
1.5%
기타 2
 
1.5%
외 2
 
1.5%
청운효자동,사직동,삼청동,가회동,부암동,평창동,무악동,교남동,혜화동 1
 
0.8%
사당2동,흑석동 1
 
0.8%
영등포본동,도림동,신길1동,신길4동 1
 
0.8%
신길3동,신길5동,신길6동,신길7동 1
 
0.8%
노량진1동,노량진2동 1
 
0.8%
상도1동,상도2동,상도3동,상도4동 1
 
0.8%
Other values (115) 115
87.1%
2023-12-11T13:11:25.221826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 424
21.2%
, 317
 
15.9%
1 89
 
4.5%
2 84
 
4.2%
3 43
 
2.2%
신 39
 
2.0%
가 31
 
1.6%
4 29
 
1.5%
계 22
 
1.1%
상 20
 
1.0%
Other values (191) 901
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1379
69.0%
Other Punctuation 318
 
15.9%
Decimal Number 281
 
14.1%
Space Separator 18
 
0.9%
Math Symbol 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 424
30.7%
신 39
 
2.8%
가 31
 
2.2%
계 22
 
1.6%
상 20
 
1.5%
정 18
 
1.3%
곡 18
 
1.3%
방 18
 
1.3%
성 17
 
1.2%
산 16
 
1.2%
Other values (177) 756
54.8%
Decimal Number
ValueCountFrequency (%)
1 89
31.7%
2 84
29.9%
3 43
15.3%
4 29
 
10.3%
5 11
 
3.9%
6 7
 
2.5%
7 7
 
2.5%
0 4
 
1.4%
8 4
 
1.4%
9 3
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 317
99.7%
? 1
 
0.3%
Space Separator
ValueCountFrequency (%)
18
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1379
69.0%
Common 620
31.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 424
30.7%
신 39
 
2.8%
가 31
 
2.2%
계 22
 
1.6%
상 20
 
1.5%
정 18
 
1.3%
곡 18
 
1.3%
방 18
 
1.3%
성 17
 
1.2%
산 16
 
1.2%
Other values (177) 756
54.8%
Common
ValueCountFrequency (%)
, 317
51.1%
1 89
 
14.4%
2 84
 
13.5%
3 43
 
6.9%
4 29
 
4.7%
18
 
2.9%
5 11
 
1.8%
6 7
 
1.1%
7 7
 
1.1%
0 4
 
0.6%
Other values (4) 11
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1379
69.0%
ASCII 620
31.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 424
30.7%
신 39
 
2.8%
가 31
 
2.2%
계 22
 
1.6%
상 20
 
1.5%
정 18
 
1.3%
곡 18
 
1.3%
방 18
 
1.3%
성 17
 
1.2%
산 16
 
1.2%
Other values (177) 756
54.8%
ASCII
ValueCountFrequency (%)
, 317
51.1%
1 89
 
14.4%
2 84
 
13.5%
3 43
 
6.9%
4 29
 
4.7%
18
 
2.9%
5 11
 
1.8%
6 7
 
1.1%
7 7
 
1.1%
0 4
 
0.6%
Other values (4) 11
 
1.8%

비고
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing110
Missing (%)96.5%
Memory size1.0 KiB
2023-12-11T13:11:25.403461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length17
Min length14

Characters and Unicode

Total characters68
Distinct characters22
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

Unique4 ?
Unique (%)100.0%

Sample

1st row재활용 일부 직영(중공2동,군자동)
2nd row재활용 일부 직영(중곡4동)
3rd row재활용 일부 직영(화양동)
4th row재활용 일부 직영(자양1동,자양4동)
ValueCountFrequency (%)
재활용 4
33.3%
일부 4
33.3%
직영(중공2동,군자동 1
 
8.3%
직영(중곡4동 1
 
8.3%
직영(화양동 1
 
8.3%
직영(자양1동,자양4동 1
 
8.3%
2023-12-11T13:11:25.746257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8
 
11.8%
동 6
 
8.8%
재 4
 
5.9%
직 4
 
5.9%
활 4
 
5.9%
영 4
 
5.9%
( 4
 
5.9%
부 4
 
5.9%
일 4
 
5.9%
용 4
 
5.9%
Other values (12) 22
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46
67.6%
Space Separator 8
 
11.8%
Open Punctuation 4
 
5.9%
Close Punctuation 4
 
5.9%
Decimal Number 4
 
5.9%
Other Punctuation 2
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6
13.0%
재 4
8.7%
직 4
8.7%
활 4
8.7%
영 4
8.7%
부 4
8.7%
일 4
8.7%
용 4
8.7%
양 3
6.5%
자 3
6.5%
Other values (5) 6
13.0%
Decimal Number
ValueCountFrequency (%)
4 2
50.0%
2 1
25.0%
1 1
25.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46
67.6%
Common 22
32.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6
13.0%
재 4
8.7%
직 4
8.7%
활 4
8.7%
영 4
8.7%
부 4
8.7%
일 4
8.7%
용 4
8.7%
양 3
6.5%
자 3
6.5%
Other values (5) 6
13.0%
Common
ValueCountFrequency (%)
8
36.4%
( 4
18.2%
) 4
18.2%
4 2
 
9.1%
, 2
 
9.1%
2 1
 
4.5%
1 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46
67.6%
ASCII 22
32.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8
36.4%
( 4
18.2%
) 4
18.2%
4 2
 
9.1%
, 2
 
9.1%
2 1
 
4.5%
1 1
 
4.5%
Hangul
ValueCountFrequency (%)
동 6
13.0%
재 4
8.7%
직 4
8.7%
활 4
8.7%
영 4
8.7%
부 4
8.7%
일 4
8.7%
용 4
8.7%
양 3
6.5%
자 3
6.5%
Other values (5) 6
13.0%

Correlations

2023-12-11T13:11:25.856433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구비고
자치구1.000NaN
비고NaN1.000

Missing values

2023-12-11T13:11:22.372648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:11:22.501591image/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.
2023-12-11T13:11:22.981490image/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

자치구대행업체일반쓰레기,음식물쓰레기재활용대형폐기물수거지역비고
0종로구심창기업㈜YY<NA>청운효자동,사직동,삼청동,가회동,부암동,평창동,무악동,교남동,혜화동<NA>
1종로구대승기업㈜YY<NA>종로1가동,종로2가동,종로3가동,종로4가동,연지동<NA>
2종로구평아실업㈜YY<NA>종로5가동,6가동,이화동,창신1동,창신2동,창신3동,숭인1동,숭인2동<NA>
3중구거구실업YYY명동 일부<NA>
4중구동보환경YYY약수동,신당5동,동화동,황학동<NA>
5중구무한기업YYY명동 일부,을지로동<NA>
6중구민영주택YYY광희동 일부,신당동,청구동<NA>
7중구수도환경YYY필동,장충동,다산동,광희동 일부<NA>
8중구하경기업YYY소공동,회현동,중림동<NA>
9용산구㈜나선기업Y<NA>Y용산2가동,이촌1동,서빙고동<NA>
자치구대행업체일반쓰레기,음식물쓰레기재활용대형폐기물수거지역비고
104송파구방산산업(주)YYY오금동,거여1동,마천1동,마천2동,오륜동<NA>
105송파구미도정업(주)YYY가락본동,가락2동,문정1동<NA>
106송파구(주)크린써비스YYY잠실2동,잠실3동 일부,종합운동장,잠실1,2롯데,가락시장 3구역<NA>
107송파구오투환경(주)YYY장지파인타운,동남권유통단지<NA>
108송파구(주)초록숲YYY송파2동,위례동,잠실7동<NA>
109강동구초록환경개발YY<NA>강일동,길동,둔촌동<NA>
110강동구삶과환경YY<NA>명일동,암사동<NA>
111강동구클린에코YY<NA>천호동<NA>
112강동구나라환경산업YY<NA>성내동<NA>
113강동구직영YY<NA>고덕동,상일동<NA>