Overview

Dataset statistics

Number of variables8
Number of observations231
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 KiB
Average record size in memory64.6 B

Variable types

Categorical2
Text5
DateTime1

Dataset

Description서울특별시 송파구_노인복지시설에 대한 데이터로 시설구분, 시설명, 시설장, 소재지, 전화번호 등에 항목으로 제공합니다.
URLhttps://www.data.go.kr/data/15044848/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
시설구분 is highly overall correlated with 위탁운영법인High correlation
위탁운영법인 is highly overall correlated with 시설구분High correlation
시설구분 is highly imbalanced (54.7%)Imbalance
위탁운영법인 is highly imbalanced (51.0%)Imbalance
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:53:00.914247
Analysis finished2023-12-12 21:53:01.630749
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경로당
179 
재가노인복지시설
26 
노인요양공동생활가정
 
13
노인요양시설
 
9
노인복지관
 
3

Length

Max length10
Median length3
Mean length4.1212121
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경로당 179
77.5%
재가노인복지시설 26
 
11.3%
노인요양공동생활가정 13
 
5.6%
노인요양시설 9
 
3.9%
노인복지관 3
 
1.3%
재가노인지원센터 1
 
0.4%

Length

2023-12-13T06:53:01.718296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:53:01.858746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경로당 179
77.5%
재가노인복지시설 26
 
11.3%
노인요양공동생활가정 13
 
5.6%
노인요양시설 9
 
3.9%
노인복지관 3
 
1.3%
재가노인지원센터 1
 
0.4%

시설명
Text

UNIQUE 

Distinct231
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T06:53:02.127940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length6.2727273
Min length2

Characters and Unicode

Total characters1449
Distinct characters240
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)100.0%

Sample

1st row송파노인요양센터(구립)
2nd row송파치매케어센터(구립)
3rd row송파노인전문요양원(시립)
4th row청암노인요양원(법인)
5th row굿모닝케어노인전문요양원
ValueCountFrequency (%)
헬리오시티 5
 
2.1%
포도나무실버타운 2
 
0.8%
송파그린요양원 2
 
0.8%
가락골 2
 
0.8%
송파노인요양센터(구립 1
 
0.4%
제1단지 1
 
0.4%
가락우성2차아파트 1
 
0.4%
가락우성 1
 
0.4%
가락대림(아 1
 
0.4%
가락금호(아 1
 
0.4%
Other values (226) 226
93.0%
2023-12-13T06:53:02.610619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 65
 
4.5%
지 43
 
3.0%
아 39
 
2.7%
송 36
 
2.5%
센 33
 
2.3%
단 33
 
2.3%
( 32
 
2.2%
) 32
 
2.2%
이 29
 
2.0%
터 29
 
2.0%
Other values (230) 1078
74.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1264
87.2%
Decimal Number 85
 
5.9%
Open Punctuation 32
 
2.2%
Close Punctuation 32
 
2.2%
Uppercase Letter 16
 
1.1%
Space Separator 12
 
0.8%
Math Symbol 5
 
0.3%
Dash Punctuation 1
 
0.1%
Letter Number 1
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 65
 
5.1%
지 43
 
3.4%
아 39
 
3.1%
송 36
 
2.8%
센 33
 
2.6%
단 33
 
2.6%
이 29
 
2.3%
터 29
 
2.3%
어 28
 
2.2%
인 24
 
1.9%
Other values (205) 905
71.6%
Decimal Number
ValueCountFrequency (%)
1 28
32.9%
2 23
27.1%
3 14
16.5%
4 5
 
5.9%
5 5
 
5.9%
6 3
 
3.5%
8 2
 
2.4%
7 2
 
2.4%
0 2
 
2.4%
9 1
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
A 7
43.8%
B 2
 
12.5%
K 2
 
12.5%
C 1
 
6.2%
E 1
 
6.2%
I 1
 
6.2%
S 1
 
6.2%
P 1
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1264
87.2%
Common 167
 
11.5%
Latin 18
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 65
 
5.1%
지 43
 
3.4%
아 39
 
3.1%
송 36
 
2.8%
센 33
 
2.6%
단 33
 
2.6%
이 29
 
2.3%
터 29
 
2.3%
어 28
 
2.2%
인 24
 
1.9%
Other values (205) 905
71.6%
Common
ValueCountFrequency (%)
( 32
19.2%
) 32
19.2%
1 28
16.8%
2 23
13.8%
3 14
8.4%
12
 
7.2%
4 5
 
3.0%
5 5
 
3.0%
+ 5
 
3.0%
6 3
 
1.8%
Other values (5) 8
 
4.8%
Latin
ValueCountFrequency (%)
A 7
38.9%
B 2
 
11.1%
K 2
 
11.1%
C 1
 
5.6%
E 1
 
5.6%
I 1
 
5.6%
S 1
 
5.6%
P 1
 
5.6%
Ⅱ 1
 
5.6%
e 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1264
87.2%
ASCII 184
 
12.7%
Number Forms 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 65
 
5.1%
지 43
 
3.4%
아 39
 
3.1%
송 36
 
2.8%
센 33
 
2.6%
단 33
 
2.6%
이 29
 
2.3%
터 29
 
2.3%
어 28
 
2.2%
인 24
 
1.9%
Other values (205) 905
71.6%
ASCII
ValueCountFrequency (%)
( 32
17.4%
) 32
17.4%
1 28
15.2%
2 23
12.5%
3 14
7.6%
12
 
6.5%
A 7
 
3.8%
4 5
 
2.7%
5 5
 
2.7%
+ 5
 
2.7%
Other values (14) 21
11.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

시설장
Text

Distinct222
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T06:53:02.979292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.987013
Min length2

Characters and Unicode

Total characters690
Distinct characters141
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

Unique214 ?
Unique (%)92.6%

Sample

1st row권영미
2nd row이경희
3rd row김나현
4th row이성희
5th row홍월란
ValueCountFrequency (%)
권영미 3
 
1.3%
이성희 2
 
0.9%
강정숙 2
 
0.9%
조아영 2
 
0.9%
하영근 2
 
0.9%
고춘옥 2
 
0.9%
이천우 2
 
0.9%
이경수 2
 
0.9%
민윤례 1
 
0.4%
박복순 1
 
0.4%
Other values (212) 212
91.8%
2023-12-13T06:53:03.492920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 41
 
5.9%
김 34
 
4.9%
영 29
 
4.2%
성 19
 
2.8%
정 18
 
2.6%
순 18
 
2.6%
최 17
 
2.5%
숙 17
 
2.5%
박 16
 
2.3%
희 15
 
2.2%
Other values (131) 466
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 690
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 41
 
5.9%
김 34
 
4.9%
영 29
 
4.2%
성 19
 
2.8%
정 18
 
2.6%
순 18
 
2.6%
최 17
 
2.5%
숙 17
 
2.5%
박 16
 
2.3%
희 15
 
2.2%
Other values (131) 466
67.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 690
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 41
 
5.9%
김 34
 
4.9%
영 29
 
4.2%
성 19
 
2.8%
정 18
 
2.6%
순 18
 
2.6%
최 17
 
2.5%
숙 17
 
2.5%
박 16
 
2.3%
희 15
 
2.2%
Other values (131) 466
67.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 690
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 41
 
5.9%
김 34
 
4.9%
영 29
 
4.2%
성 19
 
2.8%
정 18
 
2.6%
순 18
 
2.6%
최 17
 
2.5%
숙 17
 
2.5%
박 16
 
2.3%
희 15
 
2.2%
Other values (131) 466
67.5%

소재지
Text

Distinct219
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T06:53:03.819975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length11.683983
Min length6

Characters and Unicode

Total characters2699
Distinct characters106
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)91.8%

Sample

1st row충민로184(장지동)
2nd row백제고분로 32길 41 (삼전동)
3rd row백제고분로32길37(삼전동)
4th row성내천로 193 (마천동)
5th row성내천로16길17(오금동)
ValueCountFrequency (%)
성내천로 12
 
2.1%
거마로 9
 
1.6%
백제고분로 9
 
1.6%
위례광장로 9
 
1.6%
99 9
 
1.6%
마천2동 8
 
1.4%
올림픽로 8
 
1.4%
오금로 7
 
1.2%
송파대로 7
 
1.2%
마천동 6
 
1.1%
Other values (298) 476
85.0%
2023-12-13T06:53:04.235682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
432
 
16.0%
로 229
 
8.5%
1 171
 
6.3%
2 151
 
5.6%
길 121
 
4.5%
3 108
 
4.0%
4 90
 
3.3%
5 87
 
3.2%
동 65
 
2.4%
8 64
 
2.4%
Other values (96) 1181
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1188
44.0%
Decimal Number 875
32.4%
Space Separator 432
 
16.0%
Close Punctuation 61
 
2.3%
Open Punctuation 61
 
2.3%
Other Punctuation 42
 
1.6%
Dash Punctuation 36
 
1.3%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 229
19.3%
길 121
 
10.2%
동 65
 
5.5%
천 49
 
4.1%
대 40
 
3.4%
성 39
 
3.3%
마 38
 
3.2%
송 38
 
3.2%
층 31
 
2.6%
파 28
 
2.4%
Other values (77) 510
42.9%
Decimal Number
ValueCountFrequency (%)
1 171
19.5%
2 151
17.3%
3 108
12.3%
4 90
10.3%
5 87
9.9%
8 64
 
7.3%
6 62
 
7.1%
7 53
 
6.1%
9 48
 
5.5%
0 41
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
B 2
50.0%
F 1
25.0%
I 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 41
97.6%
. 1
 
2.4%
Space Separator
ValueCountFrequency (%)
432
100.0%
Close Punctuation
ValueCountFrequency (%)
) 61
100.0%
Open Punctuation
ValueCountFrequency (%)
( 61
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1507
55.8%
Hangul 1188
44.0%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 229
19.3%
길 121
 
10.2%
동 65
 
5.5%
천 49
 
4.1%
대 40
 
3.4%
성 39
 
3.3%
마 38
 
3.2%
송 38
 
3.2%
층 31
 
2.6%
파 28
 
2.4%
Other values (77) 510
42.9%
Common
ValueCountFrequency (%)
432
28.7%
1 171
 
11.3%
2 151
 
10.0%
3 108
 
7.2%
4 90
 
6.0%
5 87
 
5.8%
8 64
 
4.2%
6 62
 
4.1%
) 61
 
4.0%
( 61
 
4.0%
Other values (6) 220
14.6%
Latin
ValueCountFrequency (%)
B 2
50.0%
F 1
25.0%
I 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1511
56.0%
Hangul 1188
44.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
432
28.6%
1 171
 
11.3%
2 151
 
10.0%
3 108
 
7.1%
4 90
 
6.0%
5 87
 
5.8%
8 64
 
4.2%
6 62
 
4.1%
) 61
 
4.0%
( 61
 
4.0%
Other values (9) 224
14.8%
Hangul
ValueCountFrequency (%)
로 229
19.3%
길 121
 
10.2%
동 65
 
5.5%
천 49
 
4.1%
대 40
 
3.4%
성 39
 
3.3%
마 38
 
3.2%
송 38
 
3.2%
층 31
 
2.6%
파 28
 
2.4%
Other values (77) 510
42.9%

전화번호
Text

Distinct218
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T06:53:04.468554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.428571
Min length6

Characters and Unicode

Total characters2409
Distinct characters19
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

Unique213 ?
Unique (%)92.2%

Sample

1st row415-0056
2nd row2203-9401
3rd row2202-9179
4th row406-2344
5th row401-5075
ValueCountFrequency (%)
402-1005 7
 
3.0%
02-403-8330 5
 
2.2%
408-4070 2
 
0.9%
403-0311 2
 
0.9%
448-8400 2
 
0.9%
02-403-5988 1
 
0.4%
02-6476-3162 1
 
0.4%
02-430-2629 1
 
0.4%
02-448-0655 1
 
0.4%
02-407-7409 1
 
0.4%
Other values (209) 209
90.1%
2023-12-13T06:53:04.859852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 438
18.2%
- 407
16.9%
2 338
14.0%
4 331
13.7%
1 165
 
6.8%
3 162
 
6.7%
8 135
 
5.6%
7 118
 
4.9%
9 102
 
4.2%
5 101
 
4.2%
Other values (9) 112
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1988
82.5%
Dash Punctuation 407
 
16.9%
Other Letter 12
 
0.5%
Space Separator 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 438
22.0%
2 338
17.0%
4 331
16.6%
1 165
 
8.3%
3 162
 
8.1%
8 135
 
6.8%
7 118
 
5.9%
9 102
 
5.1%
5 101
 
5.1%
6 98
 
4.9%
Other Letter
ValueCountFrequency (%)
공 2
16.7%
용 2
16.7%
전 2
16.7%
화 2
16.7%
없 2
16.7%
음 2
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 407
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2397
99.5%
Hangul 12
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 438
18.3%
- 407
17.0%
2 338
14.1%
4 331
13.8%
1 165
 
6.9%
3 162
 
6.8%
8 135
 
5.6%
7 118
 
4.9%
9 102
 
4.3%
5 101
 
4.2%
Other values (3) 100
 
4.2%
Hangul
ValueCountFrequency (%)
공 2
16.7%
용 2
16.7%
전 2
16.7%
화 2
16.7%
없 2
16.7%
음 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2397
99.5%
Hangul 12
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 438
18.3%
- 407
17.0%
2 338
14.1%
4 331
13.8%
1 165
 
6.9%
3 162
 
6.8%
8 135
 
5.6%
7 118
 
4.9%
9 102
 
4.3%
5 101
 
4.2%
Other values (3) 100
 
4.2%
Hangul
ValueCountFrequency (%)
공 2
16.7%
용 2
16.7%
전 2
16.7%
화 2
16.7%
없 2
16.7%
음 2
16.7%

위탁운영법인
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
사립
128 
구립
45 
개 인
31 
사립(주택형)
 
6
(사복) 청암
 
4
Other values (15)
17 

Length

Max length14
Median length2
Mean length2.8787879
Min length2

Unique

Unique14 ?
Unique (%)6.1%

Sample

1st row(사복) 조계종봉은
2nd row(사복) 청암
3rd row(사복)천태종
4th row(사복) 청암
5th row개 인

Common Values

ValueCountFrequency (%)
사립 128
55.4%
구립 45
 
19.5%
개 인 31
 
13.4%
사립(주택형) 6
 
2.6%
(사복) 청암 4
 
1.7%
(사복) 조계종봉은 3
 
1.3%
주)에이플러스효담라이프케어 1
 
0.4%
(사복)천태종 1
 
0.4%
주식회사 케이비 1
 
0.4%
(사단)미래복지경영 1
 
0.4%
Other values (10) 10
 
4.3%

Length

2023-12-13T06:53:05.008743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사립 128
46.9%
구립 45
 
16.5%
개 31
 
11.4%
인 31
 
11.4%
사복 8
 
2.9%
사립(주택형 6
 
2.2%
청암 4
 
1.5%
조계종봉은 3
 
1.1%
사단 2
 
0.7%
주식회사케이비 1
 
0.4%
Other values (14) 14
 
5.1%

이용수용인원
Text

Distinct72
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T06:53:05.223478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.034632
Min length1

Characters and Unicode

Total characters470
Distinct characters17
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

Unique29 ?
Unique (%)12.6%

Sample

1st row130
2nd row28
3rd row93
4th row70
5th row49
ValueCountFrequency (%)
9 13
 
5.6%
21 11
 
4.7%
30 10
 
4.3%
20 8
 
3.4%
19 8
 
3.4%
25 7
 
3.0%
46 7
 
3.0%
26 6
 
2.6%
23 6
 
2.6%
22 6
 
2.6%
Other values (63) 152
65.0%
2023-12-13T06:53:05.659886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 83
17.7%
3 69
14.7%
1 64
13.6%
4 64
13.6%
5 38
8.1%
0 32
 
6.8%
9 29
 
6.2%
6 29
 
6.2%
8 21
 
4.5%
7 18
 
3.8%
Other values (7) 23
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447
95.1%
Other Letter 20
 
4.3%
Space Separator 3
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 83
18.6%
3 69
15.4%
1 64
14.3%
4 64
14.3%
5 38
8.5%
0 32
 
7.2%
9 29
 
6.5%
6 29
 
6.5%
8 21
 
4.7%
7 18
 
4.0%
Other Letter
ValueCountFrequency (%)
휴 4
20.0%
지 4
20.0%
일 3
15.0%
평 3
15.0%
균 3
15.0%
명 3
15.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 450
95.7%
Hangul 20
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 83
18.4%
3 69
15.3%
1 64
14.2%
4 64
14.2%
5 38
8.4%
0 32
 
7.1%
9 29
 
6.4%
6 29
 
6.4%
8 21
 
4.7%
7 18
 
4.0%
Hangul
ValueCountFrequency (%)
휴 4
20.0%
지 4
20.0%
일 3
15.0%
평 3
15.0%
균 3
15.0%
명 3
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 450
95.7%
Hangul 20
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 83
18.4%
3 69
15.3%
1 64
14.2%
4 64
14.2%
5 38
8.4%
0 32
 
7.1%
9 29
 
6.4%
6 29
 
6.4%
8 21
 
4.7%
7 18
 
4.0%
Hangul
ValueCountFrequency (%)
휴 4
20.0%
지 4
20.0%
일 3
15.0%
평 3
15.0%
균 3
15.0%
명 3
15.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Minimum2023-06-19 00:00:00
Maximum2023-06-19 00:00:00
2023-12-13T06:53:05.774221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:05.860052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2023-12-13T06:53:05.979604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분위탁운영법인이용수용인원
시설구분1.0000.9380.940
위탁운영법인0.9381.0000.953
이용수용인원0.9400.9531.000
2023-12-13T06:53:06.081144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분위탁운영법인
시설구분1.0000.764
위탁운영법인0.7641.000
2023-12-13T06:53:06.173913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분위탁운영법인
시설구분1.0000.764
위탁운영법인0.7641.000

Missing values

2023-12-13T06:53:01.418625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:53:01.572122image/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

시설구분시설명시설장소재지전화번호위탁운영법인이용수용인원데이터기준일자
0노인요양시설송파노인요양센터(구립)권영미충민로184(장지동)415-0056(사복) 조계종봉은1302023-06-19
1노인요양시설송파치매케어센터(구립)이경희백제고분로 32길 41 (삼전동)2203-9401(사복) 청암282023-06-19
2노인요양시설송파노인전문요양원(시립)김나현백제고분로32길37(삼전동)2202-9179(사복)천태종932023-06-19
3노인요양시설청암노인요양원(법인)이성희성내천로 193 (마천동)406-2344(사복) 청암702023-06-19
4노인요양시설굿모닝케어노인전문요양원홍월란성내천로16길17(오금동)401-5075개 인492023-06-19
5노인요양시설보눔케어요양원유진숙가락로27(석촌동)413-3275개 인1102023-06-19
6노인요양시설경복궁요양센터주현태성내천로47길 40(마천동)408-0048개 인462023-06-19
7노인요양시설KB골든라이프케어 위례 빌리지조아영위례광장로 220(위례동)6412-4820주식회사 케이비1252023-06-19
8노인요양시설봄날요양원노진수마천로 96(오금동)400-9091개 인382023-06-19
9노인요양공동생활가정송파그린요양원김광섭성내천로 208 (3층) (마천2동 76)408-4070개 인92023-06-19
시설구분시설명시설장소재지전화번호위탁운영법인이용수용인원데이터기준일자
221경로당잠실5손민송파대로 56702-412-9649사립632023-06-19
222경로당파크리오1문정일올림픽로 43502-3431-7468사립222023-06-19
223경로당파크리오2김성훈올림픽로 43502-3431-7440사립372023-06-19
224경로당파크리오3김명숙올림픽로 43502-3431-7467사립272023-06-19
225경로당파크리오4정기주올림픽로 43502-3431-7466사립312023-06-19
226경로당장미김영팔올림픽로35길 10402-423-0220사립642023-06-19
227경로당한신코아임동신올림픽로35가길 1102-423-4252사립212023-06-19
228경로당잠실I-SPACE김선숙오금로 5802-414-1114사립242023-06-19
229경로당아시아선수촌(아)최정윤올림픽로4길 1502-420-6195사립1492023-06-19
230경로당잠실7우성(아)공영애올림픽로4길 4202-414-2846사립412023-06-19