Overview

Dataset statistics

Number of variables6
Number of observations80
Missing cells54
Missing cells (%)11.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory49.6 B

Variable types

Categorical1
Text4
DateTime1

Dataset

Description서울특별시 관악구 노인복지시설에 대한 데이터로 시설유형, 시설명, 도로명주소, 전화번호, 홈페이지, 데이터기준일자 등을 제공합니다
Author공공데이터포털
URLhttps://www.data.go.kr/data/15106192/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
전화번호 has 1 (1.2%) missing valuesMissing
홈페이지 has 53 (66.2%) missing valuesMissing
시설명 has unique valuesUnique

Reproduction

Analysis started2024-04-17 22:36:33.112395
Analysis finished2024-04-17 22:36:34.176161
Duration1.06 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설유형
Categorical

Distinct7
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
데이케어센터
25 
노인요양공동생활가정
19 
노인교실
17 
노인요양시설
10 
무료급식소
7 
Other values (2)
 
2

Length

Max length10
Median length7
Mean length6.4375
Min length4

Unique

Unique2 ?
Unique (%)2.5%

Sample

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

Common Values

ValueCountFrequency (%)
데이케어센터 25
31.2%
노인요양공동생활가정 19
23.8%
노인교실 17
21.2%
노인요양시설 10
 
12.5%
무료급식소 7
 
8.8%
시니어클럽 1
 
1.2%
노인종합복지관 1
 
1.2%

Length

2024-04-18T07:36:34.251071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T07:36:34.380459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
데이케어센터 25
31.2%
노인요양공동생활가정 19
23.8%
노인교실 17
21.2%
노인요양시설 10
 
12.5%
무료급식소 7
 
8.8%
시니어클럽 1
 
1.2%
노인종합복지관 1
 
1.2%

시설명
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-04-18T07:36:34.557524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19
Mean length9.325
Min length5

Characters and Unicode

Total characters746
Distinct characters150
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st row신사동성베드로성당 시니어아카데미
2nd row청춘 아카데미
3rd row삼성산 노인교실
4th row성림 노인교실
5th row서림동현대아파트 실버교실
ValueCountFrequency (%)
노인교실 7
 
7.0%
아카데미 2
 
2.0%
요양원 2
 
2.0%
벧엘의집 2
 
2.0%
병설 2
 
2.0%
관악노인종합사회복지관 2
 
2.0%
은빛요양원 2
 
2.0%
신사동성베드로성당 1
 
1.0%
서림노인복지센터 1
 
1.0%
효드림주야간보호센터 1
 
1.0%
Other values (78) 78
78.0%
2024-04-18T07:36:34.861669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 28
 
3.8%
터 28
 
3.8%
센 28
 
3.8%
어 28
 
3.8%
요 27
 
3.6%
양 27
 
3.6%
데 26
 
3.5%
관 25
 
3.4%
케 23
 
3.1%
이 23
 
3.1%
Other values (140) 483
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 693
92.9%
Space Separator 20
 
2.7%
Open Punctuation 9
 
1.2%
Close Punctuation 9
 
1.2%
Decimal Number 9
 
1.2%
Uppercase Letter 6
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 28
 
4.0%
터 28
 
4.0%
센 28
 
4.0%
어 28
 
4.0%
요 27
 
3.9%
양 27
 
3.9%
데 26
 
3.8%
관 25
 
3.6%
케 23
 
3.3%
이 23
 
3.3%
Other values (129) 430
62.0%
Uppercase Letter
ValueCountFrequency (%)
I 2
33.3%
W 1
16.7%
C 1
16.7%
A 1
16.7%
Y 1
16.7%
Decimal Number
ValueCountFrequency (%)
2 4
44.4%
1 3
33.3%
0 2
22.2%
Space Separator
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 693
92.9%
Common 47
 
6.3%
Latin 6
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 28
 
4.0%
터 28
 
4.0%
센 28
 
4.0%
어 28
 
4.0%
요 27
 
3.9%
양 27
 
3.9%
데 26
 
3.8%
관 25
 
3.6%
케 23
 
3.3%
이 23
 
3.3%
Other values (129) 430
62.0%
Common
ValueCountFrequency (%)
20
42.6%
( 9
19.1%
) 9
19.1%
2 4
 
8.5%
1 3
 
6.4%
0 2
 
4.3%
Latin
ValueCountFrequency (%)
I 2
33.3%
W 1
16.7%
C 1
16.7%
A 1
16.7%
Y 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 693
92.9%
ASCII 53
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 28
 
4.0%
터 28
 
4.0%
센 28
 
4.0%
어 28
 
4.0%
요 27
 
3.9%
양 27
 
3.9%
데 26
 
3.8%
관 25
 
3.6%
케 23
 
3.3%
이 23
 
3.3%
Other values (129) 430
62.0%
ASCII
ValueCountFrequency (%)
20
37.7%
( 9
17.0%
) 9
17.0%
2 4
 
7.5%
1 3
 
5.7%
I 2
 
3.8%
0 2
 
3.8%
W 1
 
1.9%
C 1
 
1.9%
A 1
 
1.9%

도로명주소
Text

Distinct66
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-04-18T07:36:35.124133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length17.875
Min length15

Characters and Unicode

Total characters1430
Distinct characters66
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

Unique56 ?
Unique (%)70.0%

Sample

1st row서울특별시 관악구 관천로11길 124
2nd row서울특별시 관악구 방천길 39
3rd row서울특별시 관악구 호암로 545
4th row서울특별시 관악구 원신길 150
5th row서울특별시 관악구 신림로29길 8
ValueCountFrequency (%)
서울특별시 80
25.1%
관악구 79
24.8%
난곡로 11
 
3.4%
남부순환로 7
 
2.2%
은천로 5
 
1.6%
관악로 5
 
1.6%
신림로 4
 
1.3%
문성로 4
 
1.3%
보라매로 4
 
1.3%
35 4
 
1.3%
Other values (92) 116
36.4%
2024-04-18T07:36:35.517645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
239
16.7%
관 86
 
6.0%
악 85
 
5.9%
서 80
 
5.6%
특 80
 
5.6%
별 80
 
5.6%
시 80
 
5.6%
울 80
 
5.6%
구 79
 
5.5%
로 69
 
4.8%
Other values (56) 472
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 928
64.9%
Decimal Number 252
 
17.6%
Space Separator 239
 
16.7%
Dash Punctuation 10
 
0.7%
Modifier Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 86
9.3%
악 85
9.2%
서 80
8.6%
특 80
8.6%
별 80
8.6%
시 80
8.6%
울 80
8.6%
구 79
8.5%
로 69
 
7.4%
길 28
 
3.0%
Other values (43) 181
19.5%
Decimal Number
ValueCountFrequency (%)
1 57
22.6%
2 34
13.5%
3 31
12.3%
4 30
11.9%
6 23
9.1%
5 21
 
8.3%
9 19
 
7.5%
0 14
 
5.6%
7 13
 
5.2%
8 10
 
4.0%
Space Separator
ValueCountFrequency (%)
239
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 928
64.9%
Common 502
35.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 86
9.3%
악 85
9.2%
서 80
8.6%
특 80
8.6%
별 80
8.6%
시 80
8.6%
울 80
8.6%
구 79
8.5%
로 69
 
7.4%
길 28
 
3.0%
Other values (43) 181
19.5%
Common
ValueCountFrequency (%)
239
47.6%
1 57
 
11.4%
2 34
 
6.8%
3 31
 
6.2%
4 30
 
6.0%
6 23
 
4.6%
5 21
 
4.2%
9 19
 
3.8%
0 14
 
2.8%
7 13
 
2.6%
Other values (3) 21
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 928
64.9%
ASCII 502
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
239
47.6%
1 57
 
11.4%
2 34
 
6.8%
3 31
 
6.2%
4 30
 
6.0%
6 23
 
4.6%
5 21
 
4.2%
9 19
 
3.8%
0 14
 
2.8%
7 13
 
2.6%
Other values (3) 21
 
4.2%
Hangul
ValueCountFrequency (%)
관 86
9.3%
악 85
9.2%
서 80
8.6%
특 80
8.6%
별 80
8.6%
시 80
8.6%
울 80
8.6%
구 79
8.5%
로 69
 
7.4%
길 28
 
3.0%
Other values (43) 181
19.5%

전화번호
Text

MISSING 

Distinct72
Distinct (%)91.1%
Missing1
Missing (%)1.2%
Memory size772.0 B
2024-04-18T07:36:35.735996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.050633
Min length11

Characters and Unicode

Total characters873
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

Unique65 ?
Unique (%)82.3%

Sample

1st row02-839-1001
2nd row02-875-0354
3rd row02-857-2271
4th row02-888-1632
5th row02-851-9091
ValueCountFrequency (%)
02-866-6028 2
 
2.5%
02-888-6144 2
 
2.5%
02-856-4382 2
 
2.5%
02-857-5882 2
 
2.5%
02-888-8833 2
 
2.5%
02-851-6757 2
 
2.5%
02-858-6690 2
 
2.5%
02-875-3114 1
 
1.3%
02-855-4382 1
 
1.3%
02-877-8747 1
 
1.3%
Other values (62) 62
78.5%
2024-04-18T07:36:36.101809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 158
18.1%
8 145
16.6%
0 125
14.3%
2 116
13.3%
5 67
7.7%
7 65
7.4%
1 49
 
5.6%
6 46
 
5.3%
3 43
 
4.9%
9 33
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 715
81.9%
Dash Punctuation 158
 
18.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 145
20.3%
0 125
17.5%
2 116
16.2%
5 67
9.4%
7 65
9.1%
1 49
 
6.9%
6 46
 
6.4%
3 43
 
6.0%
9 33
 
4.6%
4 26
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 873
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 158
18.1%
8 145
16.6%
0 125
14.3%
2 116
13.3%
5 67
7.7%
7 65
7.4%
1 49
 
5.6%
6 46
 
5.3%
3 43
 
4.9%
9 33
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 873
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 158
18.1%
8 145
16.6%
0 125
14.3%
2 116
13.3%
5 67
7.7%
7 65
7.4%
1 49
 
5.6%
6 46
 
5.3%
3 43
 
4.9%
9 33
 
3.8%

홈페이지
Text

MISSING 

Distinct24
Distinct (%)88.9%
Missing53
Missing (%)66.2%
Memory size772.0 B
2024-04-18T07:36:36.294575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length20
Mean length18.37037
Min length12

Characters and Unicode

Total characters496
Distinct characters46
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)77.8%

Sample

1st rowwww.jadc.or.kr/
2nd rowwww.sangrok521.kr
3rd rowwww.smw.or.kr
4th rowwww.sillym.or.kr
5th rowwww.causwc.or.kr
ValueCountFrequency (%)
www.gamirsarang.or.kr 2
 
7.4%
www.noinjigi.org 2
 
7.4%
www.gndaycare.org 2
 
7.4%
www.강남한보리.kr 1
 
3.7%
www.sillym.or.kr 1
 
3.7%
www.gwanaksilvercare.com 1
 
3.7%
www.csdaycare.co.kr 1
 
3.7%
www.100세데이케어센터.com 1
 
3.7%
www.suho.or.kr 1
 
3.7%
www.sangrok521.kr 1
 
3.7%
Other values (14) 14
51.9%
2024-04-18T07:36:36.654451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 84
16.9%
. 67
13.5%
r 48
 
9.7%
o 42
 
8.5%
a 25
 
5.0%
c 21
 
4.2%
g 21
 
4.2%
k 20
 
4.0%
e 20
 
4.0%
n 19
 
3.8%
Other values (36) 129
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 402
81.0%
Other Punctuation 71
 
14.3%
Other Letter 12
 
2.4%
Decimal Number 9
 
1.8%
Math Symbol 1
 
0.2%
Connector Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 84
20.9%
r 48
11.9%
o 42
10.4%
a 25
 
6.2%
c 21
 
5.2%
g 21
 
5.2%
k 20
 
5.0%
e 20
 
5.0%
n 19
 
4.7%
i 17
 
4.2%
Other values (13) 85
21.1%
Other Letter
ValueCountFrequency (%)
센 1
8.3%
어 1
8.3%
케 1
8.3%
이 1
8.3%
데 1
8.3%
세 1
8.3%
보 1
8.3%
리 1
8.3%
한 1
8.3%
남 1
8.3%
Other values (2) 2
16.7%
Decimal Number
ValueCountFrequency (%)
2 2
22.2%
0 2
22.2%
1 2
22.2%
5 1
11.1%
4 1
11.1%
6 1
11.1%
Other Punctuation
ValueCountFrequency (%)
. 67
94.4%
/ 3
 
4.2%
? 1
 
1.4%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 402
81.0%
Common 82
 
16.5%
Hangul 12
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 84
20.9%
r 48
11.9%
o 42
10.4%
a 25
 
6.2%
c 21
 
5.2%
g 21
 
5.2%
k 20
 
5.0%
e 20
 
5.0%
n 19
 
4.7%
i 17
 
4.2%
Other values (13) 85
21.1%
Hangul
ValueCountFrequency (%)
센 1
8.3%
어 1
8.3%
케 1
8.3%
이 1
8.3%
데 1
8.3%
세 1
8.3%
보 1
8.3%
리 1
8.3%
한 1
8.3%
남 1
8.3%
Other values (2) 2
16.7%
Common
ValueCountFrequency (%)
. 67
81.7%
/ 3
 
3.7%
2 2
 
2.4%
0 2
 
2.4%
1 2
 
2.4%
5 1
 
1.2%
4 1
 
1.2%
6 1
 
1.2%
= 1
 
1.2%
? 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 484
97.6%
Hangul 12
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 84
17.4%
. 67
13.8%
r 48
9.9%
o 42
 
8.7%
a 25
 
5.2%
c 21
 
4.3%
g 21
 
4.3%
k 20
 
4.1%
e 20
 
4.1%
n 19
 
3.9%
Other values (24) 117
24.2%
Hangul
ValueCountFrequency (%)
센 1
8.3%
어 1
8.3%
케 1
8.3%
이 1
8.3%
데 1
8.3%
세 1
8.3%
보 1
8.3%
리 1
8.3%
한 1
8.3%
남 1
8.3%
Other values (2) 2
16.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
Minimum2023-08-04 00:00:00
Maximum2023-08-04 00:00:00
2024-04-18T07:36:36.756243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T07:36:36.833836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2024-04-18T07:36:36.895554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설유형시설명도로명주소전화번호홈페이지
시설유형1.0001.0000.1730.0000.862
시설명1.0001.0001.0001.0001.000
도로명주소0.1731.0001.0000.9990.993
전화번호0.0001.0000.9991.0001.000
홈페이지0.8621.0000.9931.0001.000

Missing values

2024-04-18T07:36:34.046243image/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.
2024-04-18T07:36:34.127560image/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

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