Overview

Dataset statistics

Number of variables9
Number of observations8750
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory666.6 KiB
Average record size in memory78.0 B

Variable types

Text1
Categorical2
Numeric6

Dataset

Description1. 시군구: 수급자 지역2. 이용급여 유형: 방문요양, 방문목욕, 방문간호, 주야간보호, 단기보호3. 연령: 65세~94세(5세 단위), 95세 이상4. 장기요양등급 판정자의 재가급여 종류별 이용 현황※ 2023.4.20.기준으로 1년 이내 장기요양 급여 비용이 지급된 수급자 현황(사망·중복 제외)으로 2023.7.3. 데이터 구축완료※ 민원인의 제공 신청에 따른 제공 건
Author국민건강보험공단
URLhttps://www.data.go.kr/data/15116280/fileData.do

Alerts

1등급 is highly overall correlated with 2등급 and 3 other fieldsHigh correlation
2등급 is highly overall correlated with 1등급 and 3 other fieldsHigh correlation
3등급 is highly overall correlated with 1등급 and 3 other fieldsHigh correlation
4등급 is highly overall correlated with 1등급 and 3 other fieldsHigh correlation
5등급 is highly overall correlated with 1등급 and 3 other fieldsHigh correlation
1등급 has 4118 (47.1%) zerosZeros
2등급 has 3072 (35.1%) zerosZeros
3등급 has 2173 (24.8%) zerosZeros
4등급 has 2246 (25.7%) zerosZeros
5등급 has 4020 (45.9%) zerosZeros
인지지원등급 has 7523 (86.0%) zerosZeros

Reproduction

Analysis started2023-12-12 11:12:19.050213
Analysis finished2023-12-12 11:12:27.643024
Duration8.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Text

Distinct250
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size68.5 KiB
2023-12-12T20:12:28.284894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.544
Min length7

Characters and Unicode

Total characters74760
Distinct characters149
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 종로구
2nd row서울특별시 종로구
3rd row서울특별시 종로구
4th row서울특별시 종로구
5th row서울특별시 종로구
ValueCountFrequency (%)
경기도 1470
 
7.9%
서울특별시 875
 
4.7%
경상북도 840
 
4.5%
경상남도 770
 
4.1%
전라남도 770
 
4.1%
강원도 630
 
3.4%
부산광역시 560
 
3.0%
충청남도 560
 
3.0%
전라북도 525
 
2.8%
충청북도 490
 
2.6%
Other values (243) 11095
59.7%
2023-12-12T20:12:29.510520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9835
 
13.2%
도 6300
 
8.4%
시 6090
 
8.1%
구 3990
 
5.3%
경 3185
 
4.3%
군 2975
 
4.0%
남 2660
 
3.6%
북 2135
 
2.9%
광 2100
 
2.8%
상 1750
 
2.3%
Other values (139) 33740
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64925
86.8%
Space Separator 9835
 
13.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 6300
 
9.7%
시 6090
 
9.4%
구 3990
 
6.1%
경 3185
 
4.9%
군 2975
 
4.6%
남 2660
 
4.1%
북 2135
 
3.3%
광 2100
 
3.2%
상 1750
 
2.7%
역 1715
 
2.6%
Other values (138) 32025
49.3%
Space Separator
ValueCountFrequency (%)
9835
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64925
86.8%
Common 9835
 
13.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 6300
 
9.7%
시 6090
 
9.4%
구 3990
 
6.1%
경 3185
 
4.9%
군 2975
 
4.6%
남 2660
 
4.1%
북 2135
 
3.3%
광 2100
 
3.2%
상 1750
 
2.7%
역 1715
 
2.6%
Other values (138) 32025
49.3%
Common
ValueCountFrequency (%)
9835
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64925
86.8%
ASCII 9835
 
13.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9835
100.0%
Hangul
ValueCountFrequency (%)
도 6300
 
9.7%
시 6090
 
9.4%
구 3990
 
6.1%
경 3185
 
4.9%
군 2975
 
4.6%
남 2660
 
4.1%
북 2135
 
3.3%
광 2100
 
3.2%
상 1750
 
2.7%
역 1715
 
2.6%
Other values (138) 32025
49.3%

이용급여 유형
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.5 KiB
방문요양
1750 
방문목욕
1750 
방문간호
1750 
주야간보호
1750 
단기보호
1750 

Length

Max length5
Median length4
Mean length4.2
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
방문요양 1750
20.0%
방문목욕 1750
20.0%
방문간호 1750
20.0%
주야간보호 1750
20.0%
단기보호 1750
20.0%

Length

2023-12-12T20:12:29.733502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:12:29.949607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
방문요양 1750
20.0%
방문목욕 1750
20.0%
방문간호 1750
20.0%
주야간보호 1750
20.0%
단기보호 1750
20.0%

연령
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.5 KiB
65-69세
1250 
70-74세
1250 
75-79세
1250 
80-84세
1250 
85-89세
1250 
Other values (2)
2500 

Length

Max length6
Median length6
Mean length5.8571429
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row65-69세
2nd row70-74세
3rd row75-79세
4th row80-84세
5th row85-89세

Common Values

ValueCountFrequency (%)
65-69세 1250
14.3%
70-74세 1250
14.3%
75-79세 1250
14.3%
80-84세 1250
14.3%
85-89세 1250
14.3%
90-94세 1250
14.3%
95세이상 1250
14.3%

Length

2023-12-12T20:12:30.187392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:12:30.884282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
65-69세 1250
14.3%
70-74세 1250
14.3%
75-79세 1250
14.3%
80-84세 1250
14.3%
85-89세 1250
14.3%
90-94세 1250
14.3%
95세이상 1250
14.3%

1등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4992
Minimum0
Maximum62
Zeros4118
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-12-12T20:12:31.089242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile12
Maximum62
Range62
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.0921051
Coefficient of variation (CV)2.037494
Kurtosis23.305838
Mean2.4992
Median Absolute Deviation (MAD)1
Skewness4.1259441
Sum21868
Variance25.929534
MonotonicityNot monotonic
2023-12-12T20:12:31.278808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 4118
47.1%
1 1502
 
17.2%
2 852
 
9.7%
3 569
 
6.5%
4 353
 
4.0%
5 252
 
2.9%
6 198
 
2.3%
7 128
 
1.5%
8 101
 
1.2%
9 78
 
0.9%
Other values (39) 599
 
6.8%
ValueCountFrequency (%)
0 4118
47.1%
1 1502
 
17.2%
2 852
 
9.7%
3 569
 
6.5%
4 353
 
4.0%
5 252
 
2.9%
6 198
 
2.3%
7 128
 
1.5%
8 101
 
1.2%
9 78
 
0.9%
ValueCountFrequency (%)
62 1
 
< 0.1%
57 1
 
< 0.1%
55 2
 
< 0.1%
52 2
 
< 0.1%
50 2
 
< 0.1%
44 1
 
< 0.1%
43 1
 
< 0.1%
41 2
 
< 0.1%
40 1
 
< 0.1%
39 6
0.1%

2등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct107
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1086857
Minimum0
Maximum145
Zeros3072
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-12-12T20:12:31.514498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile29
Maximum145
Range145
Interquartile range (IQR)6

Descriptive statistics

Standard deviation12.347687
Coefficient of variation (CV)2.0213329
Kurtosis20.835412
Mean6.1086857
Median Absolute Deviation (MAD)2
Skewness4.0144871
Sum53451
Variance152.46538
MonotonicityNot monotonic
2023-12-12T20:12:31.730513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3072
35.1%
1 1144
 
13.1%
2 797
 
9.1%
3 597
 
6.8%
4 448
 
5.1%
5 381
 
4.4%
6 288
 
3.3%
7 207
 
2.4%
8 191
 
2.2%
11 139
 
1.6%
Other values (97) 1486
17.0%
ValueCountFrequency (%)
0 3072
35.1%
1 1144
 
13.1%
2 797
 
9.1%
3 597
 
6.8%
4 448
 
5.1%
5 381
 
4.4%
6 288
 
3.3%
7 207
 
2.4%
8 191
 
2.2%
9 131
 
1.5%
ValueCountFrequency (%)
145 1
< 0.1%
119 1
< 0.1%
115 1
< 0.1%
114 1
< 0.1%
111 1
< 0.1%
108 1
< 0.1%
107 1
< 0.1%
105 2
< 0.1%
104 1
< 0.1%
103 2
< 0.1%

3등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct305
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.381371
Minimum0
Maximum536
Zeros2173
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-12-12T20:12:31.966900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q326
95-th percentile121.55
Maximum536
Range536
Interquartile range (IQR)25

Descriptive statistics

Standard deviation49.417096
Coefficient of variation (CV)1.9469829
Kurtosis17.806073
Mean25.381371
Median Absolute Deviation (MAD)6
Skewness3.6877233
Sum222087
Variance2442.0494
MonotonicityNot monotonic
2023-12-12T20:12:32.202893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2173
24.8%
1 751
 
8.6%
2 483
 
5.5%
3 374
 
4.3%
4 312
 
3.6%
5 242
 
2.8%
7 219
 
2.5%
6 201
 
2.3%
9 165
 
1.9%
8 160
 
1.8%
Other values (295) 3670
41.9%
ValueCountFrequency (%)
0 2173
24.8%
1 751
 
8.6%
2 483
 
5.5%
3 374
 
4.3%
4 312
 
3.6%
5 242
 
2.8%
6 201
 
2.3%
7 219
 
2.5%
8 160
 
1.8%
9 165
 
1.9%
ValueCountFrequency (%)
536 1
< 0.1%
506 1
< 0.1%
495 1
< 0.1%
433 1
< 0.1%
411 1
< 0.1%
401 2
< 0.1%
400 1
< 0.1%
399 1
< 0.1%
397 1
< 0.1%
394 1
< 0.1%

4등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct493
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.983429
Minimum0
Maximum1156
Zeros2246
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-12-12T20:12:32.492459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q349
95-th percentile248.55
Maximum1156
Range1156
Interquartile range (IQR)49

Descriptive statistics

Standard deviation97.814812
Coefficient of variation (CV)1.996896
Kurtosis16.453889
Mean48.983429
Median Absolute Deviation (MAD)7
Skewness3.5275373
Sum428605
Variance9567.7374
MonotonicityNot monotonic
2023-12-12T20:12:32.775946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2246
25.7%
1 637
 
7.3%
2 391
 
4.5%
3 304
 
3.5%
4 239
 
2.7%
5 219
 
2.5%
7 183
 
2.1%
6 178
 
2.0%
8 129
 
1.5%
9 122
 
1.4%
Other values (483) 4102
46.9%
ValueCountFrequency (%)
0 2246
25.7%
1 637
 
7.3%
2 391
 
4.5%
3 304
 
3.5%
4 239
 
2.7%
5 219
 
2.5%
6 178
 
2.0%
7 183
 
2.1%
8 129
 
1.5%
9 122
 
1.4%
ValueCountFrequency (%)
1156 1
< 0.1%
924 1
< 0.1%
911 1
< 0.1%
886 1
< 0.1%
875 1
< 0.1%
862 1
< 0.1%
818 2
< 0.1%
787 1
< 0.1%
780 2
< 0.1%
769 1
< 0.1%

5등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct170
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.030286
Minimum0
Maximum334
Zeros4020
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-12-12T20:12:33.024070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q311
95-th percentile58
Maximum334
Range334
Interquartile range (IQR)11

Descriptive statistics

Standard deviation23.405183
Coefficient of variation (CV)2.1219018
Kurtosis21.472672
Mean11.030286
Median Absolute Deviation (MAD)1
Skewness3.8165205
Sum96515
Variance547.8026
MonotonicityNot monotonic
2023-12-12T20:12:33.289024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4020
45.9%
1 817
 
9.3%
2 425
 
4.9%
3 318
 
3.6%
4 201
 
2.3%
5 155
 
1.8%
6 154
 
1.8%
7 144
 
1.6%
9 123
 
1.4%
8 104
 
1.2%
Other values (160) 2289
26.2%
ValueCountFrequency (%)
0 4020
45.9%
1 817
 
9.3%
2 425
 
4.9%
3 318
 
3.6%
4 201
 
2.3%
5 155
 
1.8%
6 154
 
1.8%
7 144
 
1.6%
8 104
 
1.2%
9 123
 
1.4%
ValueCountFrequency (%)
334 1
< 0.1%
288 1
< 0.1%
243 1
< 0.1%
235 1
< 0.1%
232 1
< 0.1%
212 1
< 0.1%
210 1
< 0.1%
202 1
< 0.1%
201 1
< 0.1%
194 1
< 0.1%

인지지원등급
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5888
Minimum0
Maximum37
Zeros7523
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-12-12T20:12:33.542279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1989956
Coefficient of variation (CV)3.7347071
Kurtosis49.657113
Mean0.5888
Median Absolute Deviation (MAD)0
Skewness6.0376343
Sum5152
Variance4.8355815
MonotonicityNot monotonic
2023-12-12T20:12:33.782427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 7523
86.0%
1 411
 
4.7%
2 197
 
2.3%
3 136
 
1.6%
4 100
 
1.1%
5 70
 
0.8%
6 66
 
0.8%
7 46
 
0.5%
8 37
 
0.4%
9 29
 
0.3%
Other values (18) 135
 
1.5%
ValueCountFrequency (%)
0 7523
86.0%
1 411
 
4.7%
2 197
 
2.3%
3 136
 
1.6%
4 100
 
1.1%
5 70
 
0.8%
6 66
 
0.8%
7 46
 
0.5%
8 37
 
0.4%
9 29
 
0.3%
ValueCountFrequency (%)
37 1
 
< 0.1%
34 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
27 1
 
< 0.1%
24 1
 
< 0.1%
22 2
 
< 0.1%
21 6
0.1%
19 8
0.1%
18 2
 
< 0.1%

Interactions

2023-12-12T20:12:25.658484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:20.940977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:21.984572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:22.877114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:23.735236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:24.657983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:25.830848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:21.127945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:22.157194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:23.012880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:23.893308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:24.836373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:26.076209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:21.326494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:22.308778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:23.147025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:24.052546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:25.006489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:26.304142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:21.488870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:22.435487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:23.293446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:24.195745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:25.163724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:26.534314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:21.650142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:22.582356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:23.441789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:24.344924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:25.325376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:26.782323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:21.813624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:22.733902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:23.580248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:24.490987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:25.491578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:12:33.959333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이용급여 유형연령1등급2등급3등급4등급5등급인지지원등급
이용급여 유형1.0000.0000.5970.6210.6120.4500.4270.517
연령0.0001.0000.1280.1770.2360.2530.3040.183
1등급0.5970.1281.0000.8590.7910.5800.3860.000
2등급0.6210.1770.8591.0000.8850.6780.4760.000
3등급0.6120.2360.7910.8851.0000.8180.6610.210
4등급0.4500.2530.5800.6780.8181.0000.5740.168
5등급0.4270.3040.3860.4760.6610.5741.0000.867
인지지원등급0.5170.1830.0000.0000.2100.1680.8671.000
2023-12-12T20:12:34.189743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령이용급여 유형
연령1.0000.000
이용급여 유형0.0001.000
2023-12-12T20:12:34.376765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1등급2등급3등급4등급5등급인지지원등급이용급여 유형연령
1등급1.0000.7300.6950.6650.507-0.0020.2890.064
2등급0.7301.0000.8680.8480.7020.1610.3050.090
3등급0.6950.8681.0000.9520.8440.3240.2990.122
4등급0.6650.8480.9521.0000.8800.3500.2800.136
5등급0.5070.7020.8440.8801.0000.4910.1910.159
인지지원등급-0.0020.1610.3240.3500.4911.0000.2400.093
이용급여 유형0.2890.3050.2990.2800.1910.2401.0000.000
연령0.0640.0900.1220.1360.1590.0930.0001.000

Missing values

2023-12-12T20:12:27.113680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:12:27.444959image/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

시군구이용급여 유형연령1등급2등급3등급4등급5등급인지지원등급
0서울특별시 종로구방문요양65-69세310172210
1서울특별시 종로구방문요양70-74세410194050
2서울특별시 종로구방문요양75-79세719528090
3서울특별시 종로구방문요양80-84세29349316590
4서울특별시 종로구방문요양85-89세1829110125170
5서울특별시 종로구방문요양90-94세1525736810
6서울특별시 종로구방문요양95세이상2516520
7서울특별시 종로구방문목욕65-69세011100
8서울특별시 종로구방문목욕70-74세011100
9서울특별시 종로구방문목욕75-79세010200
시군구이용급여 유형연령1등급2등급3등급4등급5등급인지지원등급
8740제주특별자치도 서귀포시주야간보호85-89세2654994912
8741제주특별자치도 서귀포시주야간보호90-94세222541121
8742제주특별자치도 서귀포시주야간보호95세이상0231030
8743제주특별자치도 서귀포시단기보호65-69세000000
8744제주특별자치도 서귀포시단기보호70-74세000000
8745제주특별자치도 서귀포시단기보호75-79세000000
8746제주특별자치도 서귀포시단기보호80-84세000000
8747제주특별자치도 서귀포시단기보호85-89세000000
8748제주특별자치도 서귀포시단기보호90-94세000000
8749제주특별자치도 서귀포시단기보호95세이상000000