Overview

Dataset statistics

Number of variables11
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory101.4 B

Variable types

Categorical2
Numeric9

Dataset

Description주: 1) 지급기준(사망자 급여실적 포함) 2) 연도말 주민등록주소지 기준 3) "계"의 급여이용수급자 및 급여제공기관은 급여종류별, 인정등급별 중복을 배제한 수 4) 2013년부터 항목별명 변경: 요양실인원->급여이용수급자, 요앙실기관-> 급여제공기관, 요양일수->급여제공일수, 총요양비->급여비용, 요양급여비->공단부담금
URLhttps://www.data.go.kr/data/15102780/fileData.do

Alerts

방문요양 is highly overall correlated with 방문목욕 and 7 other fieldsHigh correlation
방문목욕 is highly overall correlated with 방문요양 and 7 other fieldsHigh correlation
방문간호 is highly overall correlated with 방문요양 and 7 other fieldsHigh correlation
주야간보호 is highly overall correlated with 방문요양 and 7 other fieldsHigh correlation
단기보호 is highly overall correlated with 방문요양 and 7 other fieldsHigh correlation
복지용구 is highly overall correlated with 방문요양 and 7 other fieldsHigh correlation
노인요양시설 is highly overall correlated with 방문요양 and 7 other fieldsHigh correlation
노인요양공동생활가정 is highly overall correlated with 방문요양 and 7 other fieldsHigh correlation
통합재가서비스 is highly overall correlated with 방문요양 and 7 other fieldsHigh correlation
주야간보호 has unique valuesUnique
방문요양 has 5 (16.7%) zerosZeros
방문목욕 has 5 (16.7%) zerosZeros
방문간호 has 5 (16.7%) zerosZeros
복지용구 has 6 (20.0%) zerosZeros
통합재가서비스 has 5 (16.7%) zerosZeros

Reproduction

Analysis started2023-12-12 15:05:46.009985
Analysis finished2023-12-12 15:05:55.132601
Duration9.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분1
Categorical

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
1등급
5 
2등급
5 
3등급
5 
4등급
5 
5등급
5 

Length

Max length6
Median length3
Mean length3.5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1등급
2nd row1등급
3rd row1등급
4th row1등급
5th row1등급

Common Values

ValueCountFrequency (%)
1등급 5
16.7%
2등급 5
16.7%
3등급 5
16.7%
4등급 5
16.7%
5등급 5
16.7%
인지지원등급 5
16.7%

Length

2023-12-13T00:05:55.222223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:05:55.356299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1등급 5
16.7%
2등급 5
16.7%
3등급 5
16.7%
4등급 5
16.7%
5등급 5
16.7%
인지지원등급 5
16.7%

구분2
Categorical

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
급여이용수급자
6 
급여제공기관
6 
급여제공일수
6 
급여비용
6 
공단부담금
6 

Length

Max length7
Median length6
Mean length5.6
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row급여이용수급자
2nd row급여제공기관
3rd row급여제공일수
4th row급여비용
5th row공단부담금

Common Values

ValueCountFrequency (%)
급여이용수급자 6
20.0%
급여제공기관 6
20.0%
급여제공일수 6
20.0%
급여비용 6
20.0%
공단부담금 6
20.0%

Length

2023-12-13T00:05:55.518839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:05:55.666047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
급여이용수급자 6
20.0%
급여제공기관 6
20.0%
급여제공일수 6
20.0%
급여비용 6
20.0%
공단부담금 6
20.0%

방문요양
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3917762 × 108
Minimum0
Maximum2.7603996 × 109
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:05:55.779295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112303
median1869046.5
Q33.3418389 × 108
95-th percentile2.0473138 × 109
Maximum2.7603996 × 109
Range2.7603996 × 109
Interquartile range (IQR)3.3417159 × 108

Descriptive statistics

Standard deviation7.2359184 × 108
Coefficient of variation (CV)2.1333714
Kurtosis5.9948537
Mean3.3917762 × 108
Median Absolute Deviation (MAD)1869046.5
Skewness2.569599
Sum1.0175329 × 1010
Variance5.2358516 × 1017
MonotonicityNot monotonic
2023-12-13T00:05:55.896429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 5
 
16.7%
20064 1
 
3.3%
1322706860 1
 
3.3%
395947626 1
 
3.3%
428655968 1
 
3.3%
9044288 1
 
3.3%
11143 1
 
3.3%
65986 1
 
3.3%
2534411539 1
 
3.3%
2760399600 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0 5
16.7%
8700 1
 
3.3%
11143 1
 
3.3%
11705 1
 
3.3%
14097 1
 
3.3%
14655 1
 
3.3%
20064 1
 
3.3%
47441 1
 
3.3%
65986 1
 
3.3%
178370 1
 
3.3%
ValueCountFrequency (%)
2760399600 1
3.3%
2534411539 1
3.3%
1451972075 1
3.3%
1322706860 1
3.3%
428655968 1
3.3%
419211052 1
3.3%
395947626 1
3.3%
381017308 1
3.3%
193683649 1
3.3%
175411519 1
3.3%

방문목욕
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13398321
Minimum0
Maximum1.1191601 × 108
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:05:56.032963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12788
median84517.5
Q311483934
95-th percentile81177988
Maximum1.1191601 × 108
Range1.1191601 × 108
Interquartile range (IQR)11481146

Descriptive statistics

Standard deviation29026360
Coefficient of variation (CV)2.1664177
Kurtosis6.3738713
Mean13398321
Median Absolute Deviation (MAD)84517.5
Skewness2.6258206
Sum4.0194964 × 108
Variance8.425296 × 1014
MonotonicityNot monotonic
2023-12-13T00:05:56.172072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 5
 
16.7%
4169 1
 
3.3%
50363629 1
 
3.3%
18586910 1
 
3.3%
20272773 1
 
3.3%
265601 1
 
3.3%
1652 1
 
3.3%
13164 1
 
3.3%
102400889 1
 
3.3%
111916009 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0 5
16.7%
1652 1
 
3.3%
1907 1
 
3.3%
2658 1
 
3.3%
3178 1
 
3.3%
3393 1
 
3.3%
4169 1
 
3.3%
9741 1
 
3.3%
13164 1
 
3.3%
33286 1
 
3.3%
ValueCountFrequency (%)
111916009 1
3.3%
102400889 1
3.3%
55238886 1
3.3%
50363629 1
3.3%
20272773 1
3.3%
18586910 1
3.3%
14439449 1
3.3%
13098708 1
3.3%
6639611 1
3.3%
6026301 1
3.3%

방문간호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2450149.3
Minimum0
Maximum13782543
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:05:56.289163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1512.5
median21774.5
Q34088898.5
95-th percentile11585536
Maximum13782543
Range13782543
Interquartile range (IQR)4088386

Descriptive statistics

Standard deviation4173202.6
Coefficient of variation (CV)1.7032442
Kurtosis1.4997185
Mean2450149.3
Median Absolute Deviation (MAD)21774.5
Skewness1.6318537
Sum73504478
Variance1.741562 × 1013
MonotonicityNot monotonic
2023-12-13T00:05:56.420375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 5
 
16.7%
3448 1
 
3.3%
9407099 1
 
3.3%
1735070 1
 
3.3%
1902998 1
 
3.3%
35908 1
 
3.3%
325 1
 
3.3%
1139 1
 
3.3%
12573363 1
 
3.3%
13782543 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0 5
16.7%
325 1
 
3.3%
486 1
 
3.3%
505 1
 
3.3%
535 1
 
3.3%
549 1
 
3.3%
1139 1
 
3.3%
3448 1
 
3.3%
3505 1
 
3.3%
5987 1
 
3.3%
ValueCountFrequency (%)
13782543 1
3.3%
12573363 1
3.3%
10378193 1
3.3%
9407099 1
3.3%
6747678 1
3.3%
6092700 1
3.3%
5317145 1
3.3%
4817532 1
3.3%
1902998 1
3.3%
1735070 1
3.3%

주야간보호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2551262 × 108
Minimum696
Maximum9.233001 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:05:56.570119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum696
5-th percentile1702.05
Q16710
median3428852.5
Q340905694
95-th percentile6.9661659 × 108
Maximum9.233001 × 108
Range9.232994 × 108
Interquartile range (IQR)40898984

Descriptive statistics

Standard deviation2.5693241 × 108
Coefficient of variation (CV)2.0470644
Kurtosis3.7449634
Mean1.2551262 × 108
Median Absolute Deviation (MAD)3425459.5
Skewness2.1436629
Sum3.7653785 × 109
Variance6.6014264 × 1016
MonotonicityNot monotonic
2023-12-13T00:05:56.714540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
699 1
 
3.3%
5187 1
 
3.3%
33104813 1
 
3.3%
36165996 1
 
3.3%
652879 1
 
3.3%
3858 1
 
3.3%
11279 1
 
3.3%
470801555 1
 
3.3%
515161972 1
 
3.3%
8871823 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
696 1
3.3%
699 1
3.3%
2928 1
3.3%
3858 1
3.3%
4921 1
3.3%
5020 1
3.3%
5083 1
3.3%
5187 1
3.3%
11279 1
3.3%
41769 1
3.3%
ValueCountFrequency (%)
923300098 1
3.3%
845079458 1
3.3%
515161972 1
3.3%
470801555 1
3.3%
421818278 1
3.3%
385461307 1
3.3%
46541518 1
3.3%
42485594 1
3.3%
36165996 1
3.3%
33104813 1
3.3%

단기보호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473628.63
Minimum8
Maximum2845111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:05:56.830335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile24.3
Q182.75
median4183
Q3490229.75
95-th percentile2602745.9
Maximum2845111
Range2845103
Interquartile range (IQR)490147

Descriptive statistics

Standard deviation895023
Coefficient of variation (CV)1.8897147
Kurtosis2.4257393
Mean473628.63
Median Absolute Deviation (MAD)4174.5
Skewness1.9481221
Sum14208859
Variance8.0106617 × 1011
MonotonicityNot monotonic
2023-12-13T00:05:56.989480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
65 2
 
6.7%
85 1
 
3.3%
43 1
 
3.3%
3450 1
 
3.3%
3605 1
 
3.3%
8 1
 
3.3%
9 1
 
3.3%
546823 1
 
3.3%
593986 1
 
3.3%
10790 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
8 1
3.3%
9 1
3.3%
43 1
3.3%
65 2
6.7%
69 1
3.3%
81 1
3.3%
82 1
3.3%
85 1
3.3%
225 1
3.3%
264 1
3.3%
ValueCountFrequency (%)
2845111 1
3.3%
2607335 1
3.3%
2597137 1
3.3%
2388172 1
3.3%
981324 1
3.3%
900871 1
3.3%
593986 1
3.3%
546823 1
3.3%
320450 1
3.3%
292112 1
3.3%

복지용구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18744876
Minimum0
Maximum1.3557028 × 108
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:05:57.132680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11435.25
median50068
Q318800153
95-th percentile1.0903096 × 108
Maximum1.3557028 × 108
Range1.3557028 × 108
Interquartile range (IQR)18798718

Descriptive statistics

Standard deviation37636305
Coefficient of variation (CV)2.0078182
Kurtosis4.1638082
Mean18744876
Median Absolute Deviation (MAD)50068
Skewness2.2611842
Sum5.623463 × 108
Variance1.4164914 × 1015
MonotonicityNot monotonic
2023-12-13T00:05:57.284406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 6
 
20.0%
25032 1
 
3.3%
1610 1
 
3.3%
1343777 1
 
3.3%
1469502 1
 
3.3%
1001 1
 
3.3%
3818 1
 
3.3%
20195929 1
 
3.3%
21977120 1
 
3.3%
1508 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
0 6
20.0%
1001 1
 
3.3%
1411 1
 
3.3%
1508 1
 
3.3%
1530 1
 
3.3%
1588 1
 
3.3%
1610 1
 
3.3%
3818 1
 
3.3%
25032 1
 
3.3%
49473 1
 
3.3%
ValueCountFrequency (%)
135570282 1
3.3%
124172102 1
3.3%
90525110 1
3.3%
82024194 1
3.3%
29920878 1
3.3%
26898101 1
3.3%
21977120 1
3.3%
20195929 1
3.3%
14612826 1
3.3%
13090368 1
3.3%

노인요양시설
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8564933 × 108
Minimum4
Maximum1.6269594 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:05:57.420054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile436.9
Q14826.75
median724449
Q33.24996 × 108
95-th percentile1.4227084 × 109
Maximum1.6269594 × 109
Range1.6269594 × 109
Interquartile range (IQR)3.2499117 × 108

Descriptive statistics

Standard deviation5.1606438 × 108
Coefficient of variation (CV)1.8066361
Kurtosis1.3180742
Mean2.8564933 × 108
Median Absolute Deviation (MAD)724445
Skewness1.6613642
Sum8.56948 × 109
Variance2.6632245 × 1017
MonotonicityNot monotonic
2023-12-13T00:05:57.568153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 2
 
6.7%
23963 1
 
3.3%
4154 1
 
3.3%
58593 1
 
3.3%
64157 1
 
3.3%
966 1
 
3.3%
82834354 1
 
3.3%
92100968 1
 
3.3%
1354429 1
 
3.3%
2527 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
4 2
6.7%
966 1
3.3%
2527 1
3.3%
4154 1
3.3%
4482 1
3.3%
4519 1
3.3%
4521 1
3.3%
5744 1
3.3%
23963 1
3.3%
51534 1
3.3%
ValueCountFrequency (%)
1626959387 1
3.3%
1466529724 1
3.3%
1369149076 1
3.3%
1235247284 1
3.3%
938221159 1
3.3%
845622585 1
3.3%
446265014 1
3.3%
402627677 1
3.3%
92100968 1
3.3%
82834354 1
3.3%

노인요양공동생활가정
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22074761
Minimum1
Maximum1.1889745 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:05:57.727418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29.35
Q11878.5
median57970.5
Q328995948
95-th percentile1.0532228 × 108
Maximum1.1889745 × 108
Range1.1889745 × 108
Interquartile range (IQR)28994070

Descriptive statistics

Standard deviation38881439
Coefficient of variation (CV)1.7613527
Kurtosis0.87648915
Mean22074761
Median Absolute Deviation (MAD)57969.5
Skewness1.5510139
Sum6.6224284 × 108
Variance1.5117663 × 1015
MonotonicityNot monotonic
2023-12-13T00:05:57.882528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 2
 
6.7%
2360 1
 
3.3%
1225 1
 
3.3%
3944 1
 
3.3%
4649 1
 
3.3%
64 1
 
3.3%
6661180 1
 
3.3%
7242022 1
 
3.3%
107806 1
 
3.3%
432 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
1 2
6.7%
64 1
3.3%
432 1
3.3%
516 1
3.3%
1225 1
3.3%
1756 1
3.3%
1867 1
3.3%
1913 1
3.3%
2360 1
3.3%
3944 1
3.3%
ValueCountFrequency (%)
118897451 1
3.3%
108302065 1
3.3%
101680323 1
3.3%
93033915 1
3.3%
76162874 1
3.3%
69362411 1
3.3%
39628515 1
3.3%
36247257 1
3.3%
7242022 1
3.3%
6661180 1
3.3%

통합재가서비스
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean389234.7
Minimum0
Maximum3030676
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:05:58.029198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119.5
median1266.5
Q3289647.75
95-th percentile2184373
Maximum3030676
Range3030676
Interquartile range (IQR)289628.25

Descriptive statistics

Standard deviation796809.38
Coefficient of variation (CV)2.047118
Kurtosis5.2945058
Mean389234.7
Median Absolute Deviation (MAD)1266.5
Skewness2.4009041
Sum11677041
Variance6.3490519 × 1011
MonotonicityNot monotonic
2023-12-13T00:05:58.477768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 5
 
16.7%
17 1
 
3.3%
1369273 1
 
3.3%
909863 1
 
3.3%
1005504 1
 
3.3%
15745 1
 
3.3%
27 1
 
3.3%
191 1
 
3.3%
2732662 1
 
3.3%
3030676 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0 5
16.7%
9 1
 
3.3%
14 1
 
3.3%
17 1
 
3.3%
27 1
 
3.3%
30 1
 
3.3%
31 1
 
3.3%
52 1
 
3.3%
191 1
 
3.3%
215 1
 
3.3%
ValueCountFrequency (%)
3030676 1
3.3%
2732662 1
3.3%
1514242 1
3.3%
1369273 1
3.3%
1005504 1
3.3%
909863 1
3.3%
363834 1
3.3%
328013 1
3.3%
174552 1
3.3%
158040 1
3.3%

Interactions

2023-12-13T00:05:53.936290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:46.405420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.366112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.301059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.156103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.917435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.811932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:52.069057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.099541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:54.046313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:46.502513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.447319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.387775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.229932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.025363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.915903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:52.180428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.183554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:54.139926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:46.607354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.545640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.490963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.311982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.124851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:51.014669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:52.302852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.290427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:54.234327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:46.712806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.644812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.592936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.398502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.229379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:51.110661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:52.465806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.384174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:54.320545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:46.801005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.755736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.680126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.467896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.321065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:51.199421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:52.573588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.483784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:54.408345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:46.914350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.849430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.757826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.565206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.418450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:51.301618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:52.671363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.577689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:54.508685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.026040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.979504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.846939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.647331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.513748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:51.412793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:52.783596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.677167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:54.631430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.146663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.083238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.962056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.741081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.626393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:51.525161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:52.890507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.769538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:54.736585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:47.261856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:48.190742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.064796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.829716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.722621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:51.961970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.012530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:53.848799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:05:58.593525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1구분2방문요양방문목욕방문간호주야간보호단기보호복지용구노인요양시설노인요양공동생활가정통합재가서비스
구분11.0000.0000.4280.6080.6070.6310.6480.4580.7530.4780.603
구분20.0001.0000.5400.2860.2910.0000.4230.5610.1720.2420.157
방문요양0.4280.5401.0001.0001.0000.8910.7820.9820.8320.8260.988
방문목욕0.6080.2861.0001.0001.0000.9940.9190.9370.9700.9131.000
방문간호0.6070.2911.0001.0001.0001.0000.9100.9130.9340.9771.000
주야간보호0.6310.0000.8910.9941.0001.0000.8880.8910.8960.8451.000
단기보호0.6480.4230.7820.9190.9100.8881.0000.8390.9860.9080.893
복지용구0.4580.5610.9820.9370.9130.8910.8391.0000.8610.9220.991
노인요양시설0.7530.1720.8320.9700.9340.8960.9860.8611.0001.0000.897
노인요양공동생활가정0.4780.2420.8260.9130.9770.8450.9080.9221.0001.0000.891
통합재가서비스0.6030.1570.9881.0001.0001.0000.8930.9910.8970.8911.000
2023-12-13T00:05:58.732070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1구분2
구분11.0000.000
구분20.0001.000
2023-12-13T00:05:58.829846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방문요양방문목욕방문간호주야간보호단기보호복지용구노인요양시설노인요양공동생활가정통합재가서비스구분1구분2
방문요양1.0000.9990.9830.7450.9210.5970.9350.9350.9960.2910.216
방문목욕0.9991.0000.9830.7440.9200.5950.9340.9340.9950.4160.221
방문간호0.9830.9831.0000.6950.9120.5880.9430.9470.9760.4010.161
주야간보호0.7450.7440.6951.0000.8870.6300.8310.8050.7540.4450.000
단기보호0.9210.9200.9120.8871.0000.6640.9900.9800.9160.2710.286
복지용구0.5970.5950.5880.6300.6641.0000.6570.6460.5900.3160.227
노인요양시설0.9350.9340.9430.8310.9900.6571.0000.9940.9270.3540.081
노인요양공동생활가정0.9350.9340.9470.8050.9800.6460.9941.0000.9270.2900.122
통합재가서비스0.9960.9950.9760.7540.9160.5900.9270.9271.0000.4480.000
구분10.2910.4160.4010.4450.2710.3160.3540.2900.4481.0000.000
구분20.2160.2210.1610.0000.2860.2270.0810.1220.0000.0001.000

Missing values

2023-12-13T00:05:54.900894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:05:55.072839image/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

구분1구분2방문요양방문목욕방문간호주야간보호단기보호복지용구노인요양시설노인요양공동생활가정통합재가서비스
01등급급여이용수급자2006441693448699852503223963236017
11등급급여제공기관87001907486696431411415412259
21등급급여제공일수3397286104720125575919814761056000595070612074
31등급급여비용1936836496639611674767867538033204501461282644626501439628515174552
41등급공단부담금1754115196026301609270061695832921121309036840262767736247257158040
52등급급여이용수급자474419741350549212645066351534487252
62등급급여제공기관11705265850529286915304482175614
72등급급여제공일수77828022120871015866881221581201268895110589294711
82등급급여비용419211052144394495317145465415189813242992087893822115976162874363834
92등급공단부담금381017308130987084817532424855949008712689810184562258569362411328013
구분1구분2방문요양방문목욕방문간호주야간보호단기보호복지용구노인요양시설노인요양공동생활가정통합재가서비스
205등급급여이용수급자6598613164113954386225494735744516191
215등급급여제공기관1114316523255020651508252743227
225등급급여제공일수9044288265601359088871823107900135442910780615745
235등급급여비용428655968202727731902998515161972593986219771209210096872420221005504
245등급공단부담금39594762618586910173507047080155554682320195929828343546661180909863
25인지지원등급급여이용수급자0001127993818410
26인지지원등급급여제공기관000385881001410
27인지지원등급급여제공일수000652879650966640
28인지지원등급급여비용00036165996360514695026415746490
29인지지원등급공단부담금00033104813345013437775859339440