Overview

Dataset statistics

Number of variables8
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory69.2 B

Variable types

Numeric2
Categorical2
Text4

Dataset

Description경상남도 밀양시 노인복지시설 현황 대한 자료로, 시설 종류, 시설 명, 시설장 이름, 주소, 전화번호, 입소정원에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15104088/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 입소정원 and 1 other fieldsHigh correlation
입소정원 is highly overall correlated with 연번High correlation
시설종류 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
시설명 has unique valuesUnique
입소정원 has 14 (34.1%) zerosZeros

Reproduction

Analysis started2023-12-12 05:26:08.675397
Analysis finished2023-12-12 05:26:10.121197
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T14:26:10.229625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q331
95-th percentile39
Maximum41
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.57043565
Kurtosis-1.2
Mean21
Median Absolute Deviation (MAD)10
Skewness0
Sum861
Variance143.5
MonotonicityStrictly increasing
2023-12-12T14:26:10.404583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 1
 
2.4%
32 1
 
2.4%
24 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%
32 1
2.4%

시설종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
재가노인복지시설
24 
노인요양시설(개정법)
13 
노인요양공동생활가정
4 

Length

Max length11
Median length8
Mean length9.1463415
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노인요양공동생활가정
2nd row노인요양공동생활가정
3rd row노인요양공동생활가정
4th row노인요양공동생활가정
5th row노인요양시설(개정법)

Common Values

ValueCountFrequency (%)
재가노인복지시설 24
58.5%
노인요양시설(개정법) 13
31.7%
노인요양공동생활가정 4
 
9.8%

Length

2023-12-12T14:26:10.627223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:26:10.776373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재가노인복지시설 24
58.5%
노인요양시설(개정법 13
31.7%
노인요양공동생활가정 4
 
9.8%

시설명
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T14:26:11.006095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length8.9268293
Min length5

Characters and Unicode

Total characters366
Distinct characters103
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

Unique41 ?
Unique (%)100.0%

Sample

1st row호밥이네 노인요양원
2nd row사랑요양원
3rd row송남요양원
4th row섬김의 집
5th row강남요양원
ValueCountFrequency (%)
노인요양원 3
 
6.0%
재가복지센터 2
 
4.0%
한울데이케어재가복지센터 1
 
2.0%
참조은노인복지센터 1
 
2.0%
밀양 1
 
2.0%
데이케어센터 1
 
2.0%
아리아노인주간보호센터 1
 
2.0%
세화재가복지센터 1
 
2.0%
젬마재가노인복지센터 1
 
2.0%
청솔'노인복지센터 1
 
2.0%
Other values (37) 37
74.0%
2023-12-12T14:26:11.465662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
센 23
 
6.3%
터 23
 
6.3%
인 21
 
5.7%
노 20
 
5.5%
양 20
 
5.5%
원 17
 
4.6%
복 16
 
4.4%
지 16
 
4.4%
요 16
 
4.4%
가 13
 
3.6%
Other values (93) 181
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 346
94.5%
Space Separator 9
 
2.5%
Other Punctuation 4
 
1.1%
Uppercase Letter 3
 
0.8%
Open Punctuation 2
 
0.5%
Close Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 23
 
6.6%
터 23
 
6.6%
인 21
 
6.1%
노 20
 
5.8%
양 20
 
5.8%
원 17
 
4.9%
복 16
 
4.6%
지 16
 
4.6%
요 16
 
4.6%
가 13
 
3.8%
Other values (85) 161
46.5%
Uppercase Letter
ValueCountFrequency (%)
V 1
33.3%
I 1
33.3%
P 1
33.3%
Other Punctuation
ValueCountFrequency (%)
" 2
50.0%
' 2
50.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 346
94.5%
Common 17
 
4.6%
Latin 3
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 23
 
6.6%
터 23
 
6.6%
인 21
 
6.1%
노 20
 
5.8%
양 20
 
5.8%
원 17
 
4.9%
복 16
 
4.6%
지 16
 
4.6%
요 16
 
4.6%
가 13
 
3.8%
Other values (85) 161
46.5%
Common
ValueCountFrequency (%)
9
52.9%
( 2
 
11.8%
" 2
 
11.8%
) 2
 
11.8%
' 2
 
11.8%
Latin
ValueCountFrequency (%)
V 1
33.3%
I 1
33.3%
P 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 346
94.5%
ASCII 20
 
5.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
센 23
 
6.6%
터 23
 
6.6%
인 21
 
6.1%
노 20
 
5.8%
양 20
 
5.8%
원 17
 
4.9%
복 16
 
4.6%
지 16
 
4.6%
요 16
 
4.6%
가 13
 
3.8%
Other values (85) 161
46.5%
ASCII
ValueCountFrequency (%)
9
45.0%
( 2
 
10.0%
" 2
 
10.0%
) 2
 
10.0%
' 2
 
10.0%
V 1
 
5.0%
I 1
 
5.0%
P 1
 
5.0%

시설장이름
Text

Distinct35
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T14:26:11.714260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123
Distinct characters60
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

Unique32 ?
Unique (%)78.0%

Sample

1st row박호천
2nd row박성욱
3rd row김수한
4th row문초점
5th row조태자
ValueCountFrequency (%)
김두영 4
 
9.8%
김수한 3
 
7.3%
강주태 2
 
4.9%
박호천 1
 
2.4%
조휘열 1
 
2.4%
김말수 1
 
2.4%
박현환 1
 
2.4%
박정홍 1
 
2.4%
김선화 1
 
2.4%
이강우 1
 
2.4%
Other values (25) 25
61.0%
2023-12-12T14:26:12.083754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 15
 
12.2%
박 7
 
5.7%
이 6
 
4.9%
태 5
 
4.1%
상 4
 
3.3%
영 4
 
3.3%
수 4
 
3.3%
정 4
 
3.3%
두 4
 
3.3%
한 3
 
2.4%
Other values (50) 67
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 15
 
12.2%
박 7
 
5.7%
이 6
 
4.9%
태 5
 
4.1%
상 4
 
3.3%
영 4
 
3.3%
수 4
 
3.3%
정 4
 
3.3%
두 4
 
3.3%
한 3
 
2.4%
Other values (50) 67
54.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 123
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 15
 
12.2%
박 7
 
5.7%
이 6
 
4.9%
태 5
 
4.1%
상 4
 
3.3%
영 4
 
3.3%
수 4
 
3.3%
정 4
 
3.3%
두 4
 
3.3%
한 3
 
2.4%
Other values (50) 67
54.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 123
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 15
 
12.2%
박 7
 
5.7%
이 6
 
4.9%
태 5
 
4.1%
상 4
 
3.3%
영 4
 
3.3%
수 4
 
3.3%
정 4
 
3.3%
두 4
 
3.3%
한 3
 
2.4%
Other values (50) 67
54.5%

주소
Text

Distinct37
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T14:26:12.396426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length36
Mean length24.170732
Min length18

Characters and Unicode

Total characters991
Distinct characters98
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

Unique33 ?
Unique (%)80.5%

Sample

1st row경상남도 밀양시 상남면 외산길 7
2nd row경상남도 밀양시 단장면 단장로 1166-29
3rd row경상남도 밀양시 단장면 무릉3길 14-10
4th row경상남도 밀양시 상남면 인산남길 26
5th row경상남도 밀양시 삼랑진읍 청학2길 35
ValueCountFrequency (%)
경상남도 41
 
18.1%
밀양시 41
 
18.1%
무안면 6
 
2.7%
내일동 4
 
1.8%
중앙로 4
 
1.8%
단장면 4
 
1.8%
부북면 4
 
1.8%
7 3
 
1.3%
1층 3
 
1.3%
삼문동 3
 
1.3%
Other values (90) 113
50.0%
2023-12-12T14:26:12.927224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
185
18.7%
남 50
 
5.0%
상 48
 
4.8%
밀 43
 
4.3%
양 43
 
4.3%
경 41
 
4.1%
도 41
 
4.1%
시 41
 
4.1%
1 31
 
3.1%
면 24
 
2.4%
Other values (88) 444
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 595
60.0%
Space Separator 185
 
18.7%
Decimal Number 155
 
15.6%
Dash Punctuation 16
 
1.6%
Close Punctuation 14
 
1.4%
Open Punctuation 14
 
1.4%
Other Punctuation 12
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 50
 
8.4%
상 48
 
8.1%
밀 43
 
7.2%
양 43
 
7.2%
경 41
 
6.9%
도 41
 
6.9%
시 41
 
6.9%
면 24
 
4.0%
로 23
 
3.9%
길 22
 
3.7%
Other values (73) 219
36.8%
Decimal Number
ValueCountFrequency (%)
1 31
20.0%
2 22
14.2%
4 21
13.5%
6 20
12.9%
3 20
12.9%
0 13
8.4%
7 9
 
5.8%
5 8
 
5.2%
9 6
 
3.9%
8 5
 
3.2%
Space Separator
ValueCountFrequency (%)
185
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 595
60.0%
Common 396
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 50
 
8.4%
상 48
 
8.1%
밀 43
 
7.2%
양 43
 
7.2%
경 41
 
6.9%
도 41
 
6.9%
시 41
 
6.9%
면 24
 
4.0%
로 23
 
3.9%
길 22
 
3.7%
Other values (73) 219
36.8%
Common
ValueCountFrequency (%)
185
46.7%
1 31
 
7.8%
2 22
 
5.6%
4 21
 
5.3%
6 20
 
5.1%
3 20
 
5.1%
- 16
 
4.0%
) 14
 
3.5%
( 14
 
3.5%
0 13
 
3.3%
Other values (5) 40
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 595
60.0%
ASCII 396
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
185
46.7%
1 31
 
7.8%
2 22
 
5.6%
4 21
 
5.3%
6 20
 
5.1%
3 20
 
5.1%
- 16
 
4.0%
) 14
 
3.5%
( 14
 
3.5%
0 13
 
3.3%
Other values (5) 40
 
10.1%
Hangul
ValueCountFrequency (%)
남 50
 
8.4%
상 48
 
8.1%
밀 43
 
7.2%
양 43
 
7.2%
경 41
 
6.9%
도 41
 
6.9%
시 41
 
6.9%
면 24
 
4.0%
로 23
 
3.9%
길 22
 
3.7%
Other values (73) 219
36.8%

전화번호
Text

Distinct37
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T14:26:13.180871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.02439
Min length12

Characters and Unicode

Total characters493
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

Unique34 ?
Unique (%)82.9%

Sample

1st row055-353-5581
2nd row055-356-2289
3rd row055-351-1383
4th row055-351-0156
5th row055-354-3003
ValueCountFrequency (%)
055-351-1383 3
 
7.3%
055-352-1163 2
 
4.9%
055-354-1605 2
 
4.9%
055-352-1605 1
 
2.4%
055-391-0887 1
 
2.4%
055-352-8859 1
 
2.4%
055-353-2398 1
 
2.4%
055-354-2236 1
 
2.4%
055-356-0408 1
 
2.4%
055-356-8522 1
 
2.4%
Other values (27) 27
65.9%
2023-12-12T14:26:13.571319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 138
28.0%
- 82
16.6%
3 64
13.0%
0 62
12.6%
1 33
 
6.7%
8 24
 
4.9%
6 20
 
4.1%
9 20
 
4.1%
7 18
 
3.7%
2 17
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 411
83.4%
Dash Punctuation 82
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 138
33.6%
3 64
15.6%
0 62
15.1%
1 33
 
8.0%
8 24
 
5.8%
6 20
 
4.9%
9 20
 
4.9%
7 18
 
4.4%
2 17
 
4.1%
4 15
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 493
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 138
28.0%
- 82
16.6%
3 64
13.0%
0 62
12.6%
1 33
 
6.7%
8 24
 
4.9%
6 20
 
4.1%
9 20
 
4.1%
7 18
 
3.7%
2 17
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 138
28.0%
- 82
16.6%
3 64
13.0%
0 62
12.6%
1 33
 
6.7%
8 24
 
4.9%
6 20
 
4.1%
9 20
 
4.1%
7 18
 
3.7%
2 17
 
3.4%

입소정원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.02439
Minimum0
Maximum112
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T14:26:13.763713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q329
95-th percentile48
Maximum112
Range112
Interquartile range (IQR)29

Descriptive statistics

Standard deviation24.902297
Coefficient of variation (CV)1.2435983
Kurtosis5.4146446
Mean20.02439
Median Absolute Deviation (MAD)11
Skewness2.0827984
Sum821
Variance620.12439
MonotonicityNot monotonic
2023-12-12T14:26:13.915704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 14
34.1%
9 5
 
12.2%
29 4
 
9.8%
25 2
 
4.9%
14 1
 
2.4%
7 1
 
2.4%
24 1
 
2.4%
17 1
 
2.4%
30 1
 
2.4%
38 1
 
2.4%
Other values (10) 10
24.4%
ValueCountFrequency (%)
0 14
34.1%
7 1
 
2.4%
9 5
 
12.2%
11 1
 
2.4%
14 1
 
2.4%
16 1
 
2.4%
17 1
 
2.4%
24 1
 
2.4%
25 2
 
4.9%
26 1
 
2.4%
ValueCountFrequency (%)
112 1
 
2.4%
99 1
 
2.4%
48 1
 
2.4%
45 1
 
2.4%
42 1
 
2.4%
41 1
 
2.4%
40 1
 
2.4%
38 1
 
2.4%
30 1
 
2.4%
29 4
9.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-03-01
41 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-01
2nd row2023-03-01
3rd row2023-03-01
4th row2023-03-01
5th row2023-03-01

Common Values

ValueCountFrequency (%)
2023-03-01 41
100.0%

Length

2023-12-12T14:26:14.073612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:26:14.200559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-01 41
100.0%

Interactions

2023-12-12T14:26:09.620222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:26:09.091083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:26:09.791703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:26:09.506857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:26:14.303121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설종류시설명시설장이름주소전화번호입소정원
연번1.0000.9671.0000.4140.6620.6620.000
시설종류0.9671.0001.0000.0000.0000.0000.429
시설명1.0001.0001.0001.0001.0001.0001.000
시설장이름0.4140.0001.0001.0001.0001.0000.958
주소0.6620.0001.0001.0001.0000.9990.921
전화번호0.6620.0001.0001.0000.9991.0000.949
입소정원0.0000.4291.0000.9580.9210.9491.000
2023-12-12T14:26:14.424229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번입소정원시설종류
연번1.000-0.5160.823
입소정원-0.5161.0000.297
시설종류0.8230.2971.000

Missing values

2023-12-12T14:26:09.908820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:26:10.061216image/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

연번시설종류시설명시설장이름주소전화번호입소정원데이터기준일자
01노인요양공동생활가정호밥이네 노인요양원박호천경상남도 밀양시 상남면 외산길 7055-353-558192023-03-01
12노인요양공동생활가정사랑요양원박성욱경상남도 밀양시 단장면 단장로 1166-29055-356-228992023-03-01
23노인요양공동생활가정송남요양원김수한경상남도 밀양시 단장면 무릉3길 14-10055-351-138392023-03-01
34노인요양공동생활가정섬김의 집문초점경상남도 밀양시 상남면 인산남길 26055-351-015692023-03-01
45노인요양시설(개정법)강남요양원조태자경상남도 밀양시 삼랑진읍 청학2길 35055-354-3003422023-03-01
56노인요양시설(개정법)행복한 너싱홈 노인요양원박상희경상남도 밀양시 상동면 안인1길 32-13055-353-6781262023-03-01
67노인요양시설(개정법)덕인노인전문요양원김정애경상남도 밀양시 하남읍 남전3길 34-64 (외4필지)055-391-12531122023-03-01
78노인요양시설(개정법)밀양시립노인요양원곽대은경상남도 밀양시 산외면 금곡1길 40055-356-8877992023-03-01
89노인요양시설(개정법)참조은노인요양원강주태경상남도 밀양시 무안면 고사길 51055-352-1163402023-03-01
910노인요양시설(개정법)노인요양시설 효심마을이익순경상남도 밀양시 무안면 무안로 18055-351-1990292023-03-01
연번시설종류시설명시설장이름주소전화번호입소정원데이터기준일자
3132재가노인복지시설진성재가노인복지센터손진상경상남도 밀양시 중앙로 364, 6층 602호 (내일동)055-356-851202023-03-01
3233재가노인복지시설부북노인복지센터이상범경상남도 밀양시 부북면 덕곡2길 62, 1층055-356-0408172023-03-01
3334재가노인복지시설밀양제일재가방문간호센터이옥희경상남도 밀양시 밀양대로 2233 (교동)055-353-239802023-03-01
3435재가노인복지시설가득행복 재가복지센터이득미경상남도 밀양시 새미리벌로 39, 1층 (삼문동)055-352-885902023-03-01
3536재가노인복지시설권하재가복지센터최권하경상남도 밀양시 하남읍 수산중앙로 20055-391-088702023-03-01
3637재가노인복지시설우리들노인주간보호센터김두영경상남도 밀양시 용평로 49-6 (용평동)055-352-1605242023-03-01
3738재가노인복지시설희망실버 재가복지센터손기자경상남도 밀양시 삼랑진읍 청학2길 46-7055-353-797302023-03-01
3839재가노인복지시설진성복지용구의료기이종상경상남도 밀양시 중앙로 364, 4층 402호 (내일동, 송정빌딩)055-356-137902023-03-01
3940재가노인복지시설송남주간보호센터김수한경상남도 밀양시 단장면 무릉3길 14-10055-351-138372023-03-01
4041재가노인복지시설으뜸노인재가복지센터김지현경상남도 밀양시 삼문중앙로6길 30-4, 102호 (삼문동)055-356-879502023-03-01