Overview

Dataset statistics

Number of variables5
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory972.0 B
Average record size in memory46.3 B

Variable types

Categorical2
Text3

Dataset

Description2023년도 계룡시 관내의 노인의료 복지시설 현황에 대한 데이터로서 시설명, 주소, 연락처에 관한 공공데이터를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=311&beforeMenuCd=DOM_000000201001001000&publicdatapk=15093867

Alerts

구분 is highly overall correlated with 시설(급여)종류High correlation
시설(급여)종류 is highly overall correlated with 구분High correlation

Reproduction

Analysis started2024-01-09 20:48:03.253805
Analysis finished2024-01-09 20:48:03.558292
Duration0.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
재가노인복지
13 
노인의료복지
8 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재가노인복지 13
61.9%
노인의료복지 8
38.1%

Length

2024-01-10T05:48:03.614915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:48:03.698074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재가노인복지 13
61.9%
노인의료복지 8
38.1%

시설(급여)종류
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
노인요양공동 생활가정
6 
주야간보호
4 
방문요양
4 
방문목욕
3 
노인요양시설
2 
Other values (2)
2 

Length

Max length11
Median length6
Mean length6.3809524
Min length4

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row노인요양시설
2nd row노인요양시설
3rd row노인요양공동 생활가정
4th row노인요양공동 생활가정
5th row노인요양공동 생활가정

Common Values

ValueCountFrequency (%)
노인요양공동 생활가정 6
28.6%
주야간보호 4
19.0%
방문요양 4
19.0%
방문목욕 3
14.3%
노인요양시설 2
 
9.5%
단기보호 1
 
4.8%
<NA> 1
 
4.8%

Length

2024-01-10T05:48:03.795293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:48:03.895891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노인요양공동 6
22.2%
생활가정 6
22.2%
주야간보호 4
14.8%
방문요양 4
14.8%
방문목욕 3
11.1%
노인요양시설 2
 
7.4%
단기보호 1
 
3.7%
na 1
 
3.7%

시설명
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-01-10T05:48:04.048626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.8095238
Min length5

Characters and Unicode

Total characters164
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)47.6%

Sample

1st row계룡효센터
2nd row효성요양원
3rd row계룡요양원
4th row은빛요양원
5th row시온실버홈
ValueCountFrequency (%)
계룡재가주간보호센터 3
14.3%
계룡효재가노인복지센터 2
 
9.5%
은빛요양복지센터 2
 
9.5%
보배로운복지센터 2
 
9.5%
아리야재가복지센터 2
 
9.5%
계룡효센터 1
 
4.8%
효성요양원 1
 
4.8%
계룡요양원 1
 
4.8%
은빛요양원 1
 
4.8%
시온실버홈 1
 
4.8%
Other values (5) 5
23.8%
2024-01-10T05:48:04.312567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
센 14
 
8.5%
터 14
 
8.5%
복 9
 
5.5%
계 8
 
4.9%
양 8
 
4.9%
요 8
 
4.9%
지 8
 
4.9%
룡 8
 
4.9%
가 7
 
4.3%
재 7
 
4.3%
Other values (34) 73
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 164
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 14
 
8.5%
터 14
 
8.5%
복 9
 
5.5%
계 8
 
4.9%
양 8
 
4.9%
요 8
 
4.9%
지 8
 
4.9%
룡 8
 
4.9%
가 7
 
4.3%
재 7
 
4.3%
Other values (34) 73
44.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 164
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 14
 
8.5%
터 14
 
8.5%
복 9
 
5.5%
계 8
 
4.9%
양 8
 
4.9%
요 8
 
4.9%
지 8
 
4.9%
룡 8
 
4.9%
가 7
 
4.3%
재 7
 
4.3%
Other values (34) 73
44.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 164
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
센 14
 
8.5%
터 14
 
8.5%
복 9
 
5.5%
계 8
 
4.9%
양 8
 
4.9%
요 8
 
4.9%
지 8
 
4.9%
룡 8
 
4.9%
가 7
 
4.3%
재 7
 
4.3%
Other values (34) 73
44.5%

주소
Text

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-01-10T05:48:04.461235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19
Mean length14.904762
Min length10

Characters and Unicode

Total characters313
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)38.1%

Sample

1st row두마면 대둔로 1380-49
2nd row엄사면 도곡로 46-12
3rd row엄사면 번영8길 8-11
4th row엄사면 엄사중앙로 30, 5층
5th row엄사면 전원로 85
ValueCountFrequency (%)
엄사면 17
25.0%
8-11 4
 
5.9%
번영8길 4
 
5.9%
1380-49 3
 
4.4%
엄사중앙로 3
 
4.4%
대둔로 3
 
4.4%
두마면 3
 
4.4%
번영로 2
 
2.9%
12 2
 
2.9%
403호(계룡 2
 
2.9%
Other values (20) 25
36.8%
2024-01-10T05:48:04.731360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
15.0%
엄 22
 
7.0%
1 22
 
7.0%
사 22
 
7.0%
면 20
 
6.4%
로 15
 
4.8%
0 14
 
4.5%
8 12
 
3.8%
3 12
 
3.8%
번 10
 
3.2%
Other values (33) 117
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 154
49.2%
Decimal Number 90
28.8%
Space Separator 47
 
15.0%
Dash Punctuation 10
 
3.2%
Other Punctuation 8
 
2.6%
Close Punctuation 2
 
0.6%
Open Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
엄 22
14.3%
사 22
14.3%
면 20
13.0%
로 15
9.7%
번 10
 
6.5%
영 10
 
6.5%
길 6
 
3.9%
호 6
 
3.9%
중 5
 
3.2%
앙 5
 
3.2%
Other values (18) 33
21.4%
Decimal Number
ValueCountFrequency (%)
1 22
24.4%
0 14
15.6%
8 12
13.3%
3 12
13.3%
4 10
11.1%
2 7
 
7.8%
5 6
 
6.7%
9 4
 
4.4%
7 2
 
2.2%
6 1
 
1.1%
Space Separator
ValueCountFrequency (%)
47
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159
50.8%
Hangul 154
49.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
엄 22
14.3%
사 22
14.3%
면 20
13.0%
로 15
9.7%
번 10
 
6.5%
영 10
 
6.5%
길 6
 
3.9%
호 6
 
3.9%
중 5
 
3.2%
앙 5
 
3.2%
Other values (18) 33
21.4%
Common
ValueCountFrequency (%)
47
29.6%
1 22
13.8%
0 14
 
8.8%
8 12
 
7.5%
3 12
 
7.5%
- 10
 
6.3%
4 10
 
6.3%
, 8
 
5.0%
2 7
 
4.4%
5 6
 
3.8%
Other values (5) 11
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159
50.8%
Hangul 154
49.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47
29.6%
1 22
13.8%
0 14
 
8.8%
8 12
 
7.5%
3 12
 
7.5%
- 10
 
6.3%
4 10
 
6.3%
, 8
 
5.0%
2 7
 
4.4%
5 6
 
3.8%
Other values (5) 11
 
6.9%
Hangul
ValueCountFrequency (%)
엄 22
14.3%
사 22
14.3%
면 20
13.0%
로 15
9.7%
번 10
 
6.5%
영 10
 
6.5%
길 6
 
3.9%
호 6
 
3.9%
중 5
 
3.2%
앙 5
 
3.2%
Other values (18) 33
21.4%

연락처
Text

Distinct12
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-01-10T05:48:04.885305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters168
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

Unique7 ?
Unique (%)33.3%

Sample

1st row551-5629
2nd row841-4800
3rd row551-5266
4th row841-3138
5th row274-8999
ValueCountFrequency (%)
551-5266 4
19.0%
551-5629 3
14.3%
841-3138 3
14.3%
841-0328 2
9.5%
551-5116 2
9.5%
841-4800 1
 
4.8%
274-8999 1
 
4.8%
840-8275 1
 
4.8%
551-6003 1
 
4.8%
551-9011 1
 
4.8%
Other values (2) 2
9.5%
2024-01-10T05:48:05.121511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 34
20.2%
1 29
17.3%
- 21
12.5%
8 16
9.5%
2 14
8.3%
6 14
8.3%
4 11
 
6.5%
3 10
 
6.0%
0 9
 
5.4%
9 8
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147
87.5%
Dash Punctuation 21
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 34
23.1%
1 29
19.7%
8 16
10.9%
2 14
9.5%
6 14
9.5%
4 11
 
7.5%
3 10
 
6.8%
0 9
 
6.1%
9 8
 
5.4%
7 2
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 34
20.2%
1 29
17.3%
- 21
12.5%
8 16
9.5%
2 14
8.3%
6 14
8.3%
4 11
 
6.5%
3 10
 
6.0%
0 9
 
5.4%
9 8
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 34
20.2%
1 29
17.3%
- 21
12.5%
8 16
9.5%
2 14
8.3%
6 14
8.3%
4 11
 
6.5%
3 10
 
6.0%
0 9
 
5.4%
9 8
 
4.8%

Correlations

2024-01-10T05:48:05.214826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시설(급여)종류시설명주소연락처
구분1.0001.0001.0000.6130.439
시설(급여)종류1.0001.0000.0000.0000.000
시설명1.0000.0001.0001.0001.000
주소0.6130.0001.0001.0001.000
연락처0.4390.0001.0001.0001.000
2024-01-10T05:48:05.300401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시설(급여)종류
구분1.0000.882
시설(급여)종류0.8821.000
2024-01-10T05:48:05.370073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시설(급여)종류
구분1.0000.882
시설(급여)종류0.8821.000

Missing values

2024-01-10T05:48:03.453017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:48:03.526439image/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

구분시설(급여)종류시설명주소연락처
0노인의료복지노인요양시설계룡효센터두마면 대둔로 1380-49551-5629
1노인의료복지노인요양시설효성요양원엄사면 도곡로 46-12841-4800
2노인의료복지노인요양공동 생활가정계룡요양원엄사면 번영8길 8-11551-5266
3노인의료복지노인요양공동 생활가정은빛요양원엄사면 엄사중앙로 30, 5층841-3138
4노인의료복지노인요양공동 생활가정시온실버홈엄사면 전원로 85274-8999
5노인의료복지노인요양공동 생활가정천사요양원엄사면 엄사중앙로 30, 3층840-8275
6노인의료복지노인요양공동 생활가정평강요양원엄사면 번영3길 42-7551-6003
7노인의료복지노인요양공동 생활가정축복요양원엄사면 번영11길 4-29551-9011
8재가노인복지주야간보호계룡효재가노인복지센터두마면 대둔로 1380-49551-5629
9재가노인복지단기보호계룡효재가노인복지센터두마면 대둔로 1380-49551-5629
구분시설(급여)종류시설명주소연락처
11재가노인복지방문요양계룡재가주간보호센터엄사면 번영8길 8-11551-5266
12재가노인복지방문목욕계룡재가주간보호센터엄사면 번영8길 8-11551-5266
13재가노인복지주야간보호계룡재가주간보호센터엄사면 번영8길 8-11551-5266
14재가노인복지방문요양은빛요양복지센터엄사면 엄사중앙로30841-3138
15재가노인복지<NA>은빛요양복지센터엄사면 엄사중앙로30841-3138
16재가노인복지방문요양보배로운복지센터엄사면 번영로 12, 403호(계룡 오피스텔)841-0328
17재가노인복지방문목욕보배로운복지센터엄사면 번영로 12, 403호(계룡 오피스텔)841-0328
18재가노인복지주야간보호계룡효자부모님케어센터엄사면 엄사중앙로 110, 501호840-9112
19재가노인복지방문요양아리야재가복지센터엄사면 번영로12, 405호551-5116
20재가노인복지방문목욕아리야재가복지센터엄사면 번영로12, 405호551-5116