Overview

Dataset statistics

Number of variables9
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory82.9 B

Variable types

Text1
Categorical1
Numeric7

Dataset

Description노인장기요양보험수급자 중 65세미만 신청 인정자 노인성질병현황 주) 2008년 4월부터 노인장기요양보험 신청자 기준이며, 연도말 자격유지자 기준(사망자 제외) - 주상명 기준이며 "계"는 질병 간 중복건 제외, 기타는 이외 만성질환 및 정신질환, 감염성 질환 등 - 노인장기요양보험 신청 시 제출한 진단서 또는 의사소견서 기준 - 통합 전 한방 상병 코드를 참고용으로 기재
URLhttps://www.data.go.kr/data/15102777/fileData.do

Alerts

치매 is highly overall correlated with 파킨슨병 and 5 other fieldsHigh correlation
파킨슨병 is highly overall correlated with 치매 and 5 other fieldsHigh correlation
알츠하이머 is highly overall correlated with 치매 and 5 other fieldsHigh correlation
뇌질환 등 is highly overall correlated with 치매 and 5 other fieldsHigh correlation
중풍후유증 is highly overall correlated with 치매 and 5 other fieldsHigh correlation
진전 is highly overall correlated with 치매 and 5 other fieldsHigh correlation
기타 is highly overall correlated with 치매 and 5 other fieldsHigh correlation
치매 has unique valuesUnique
뇌질환 등 has unique valuesUnique
중풍후유증 has 2 (5.9%) zerosZeros
진전 has 7 (20.6%) zerosZeros

Reproduction

Analysis started2023-12-12 12:29:18.109775
Analysis finished2023-12-12 12:29:24.307308
Duration6.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분1
Text

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T21:29:24.458402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters68
Distinct characters21
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

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row서울
3rd row부산
4th row부산
5th row대구
ValueCountFrequency (%)
서울 2
 
5.9%
강원 2
 
5.9%
경남 2
 
5.9%
경북 2
 
5.9%
전남 2
 
5.9%
전북 2
 
5.9%
충남 2
 
5.9%
충북 2
 
5.9%
경기 2
 
5.9%
부산 2
 
5.9%
Other values (7) 14
41.2%
2023-12-12T21:29:24.788889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
전 6
 
8.8%
경 6
 
8.8%
남 6
 
8.8%
북 6
 
8.8%
산 4
 
5.9%
대 4
 
5.9%
충 4
 
5.9%
주 4
 
5.9%
울 4
 
5.9%
원 2
 
2.9%
Other values (11) 22
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 6
 
8.8%
경 6
 
8.8%
남 6
 
8.8%
북 6
 
8.8%
산 4
 
5.9%
대 4
 
5.9%
충 4
 
5.9%
주 4
 
5.9%
울 4
 
5.9%
원 2
 
2.9%
Other values (11) 22
32.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 6
 
8.8%
경 6
 
8.8%
남 6
 
8.8%
북 6
 
8.8%
산 4
 
5.9%
대 4
 
5.9%
충 4
 
5.9%
주 4
 
5.9%
울 4
 
5.9%
원 2
 
2.9%
Other values (11) 22
32.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
전 6
 
8.8%
경 6
 
8.8%
남 6
 
8.8%
북 6
 
8.8%
산 4
 
5.9%
대 4
 
5.9%
충 4
 
5.9%
주 4
 
5.9%
울 4
 
5.9%
원 2
 
2.9%
Other values (11) 22
32.4%

구분2
Categorical

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
남자
17 
여자
17 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남자
2nd row여자
3rd row남자
4th row여자
5th row남자

Common Values

ValueCountFrequency (%)
남자 17
50.0%
여자 17
50.0%

Length

2023-12-12T21:29:24.944262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:29:25.060711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남자 17
50.0%
여자 17
50.0%

치매
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean399.91176
Minimum22
Maximum1987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T21:29:25.251980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile52.05
Q1189.5
median312.5
Q3418
95-th percentile1127.65
Maximum1987
Range1965
Interquartile range (IQR)228.5

Descriptive statistics

Standard deviation390.56892
Coefficient of variation (CV)0.97663774
Kurtosis8.6848654
Mean399.91176
Median Absolute Deviation (MAD)113
Skewness2.7389392
Sum13597
Variance152544.08
MonotonicityNot monotonic
2023-12-12T21:29:25.479023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
967 1
 
2.9%
306 1
 
2.9%
362 1
 
2.9%
239 1
 
2.9%
393 1
 
2.9%
295 1
 
2.9%
315 1
 
2.9%
310 1
 
2.9%
268 1
 
2.9%
351 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
22 1
2.9%
28 1
2.9%
65 1
2.9%
95 1
2.9%
137 1
2.9%
147 1
2.9%
158 1
2.9%
164 1
2.9%
180 1
2.9%
218 1
2.9%
ValueCountFrequency (%)
1987 1
2.9%
1426 1
2.9%
967 1
2.9%
813 1
2.9%
514 1
2.9%
501 1
2.9%
466 1
2.9%
426 1
2.9%
425 1
2.9%
397 1
2.9%

파킨슨병
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.94118
Minimum5
Maximum547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T21:29:25.642884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13.9
Q171.75
median102
Q3135.5
95-th percentile421.75
Maximum547
Range542
Interquartile range (IQR)63.75

Descriptive statistics

Standard deviation125.84742
Coefficient of variation (CV)0.95381457
Kurtosis5.3133786
Mean131.94118
Median Absolute Deviation (MAD)33
Skewness2.2976896
Sum4486
Variance15837.572
MonotonicityNot monotonic
2023-12-12T21:29:25.818976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
16 2
 
5.9%
102 2
 
5.9%
306 1
 
2.9%
109 1
 
2.9%
85 1
 
2.9%
62 1
 
2.9%
97 1
 
2.9%
108 1
 
2.9%
110 1
 
2.9%
91 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
5 1
2.9%
10 1
2.9%
16 2
5.9%
48 1
2.9%
59 1
2.9%
61 1
2.9%
62 1
2.9%
71 1
2.9%
74 1
2.9%
76 1
2.9%
ValueCountFrequency (%)
547 1
2.9%
529 1
2.9%
364 1
2.9%
306 1
2.9%
181 1
2.9%
167 1
2.9%
155 1
2.9%
139 1
2.9%
136 1
2.9%
134 1
2.9%

알츠하이머
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.647059
Minimum1
Maximum161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T21:29:26.008110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.65
Q117
median22.5
Q330.75
95-th percentile102.65
Maximum161
Range160
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation33.992764
Coefficient of variation (CV)1.0741208
Kurtosis7.1368751
Mean31.647059
Median Absolute Deviation (MAD)8
Skewness2.576562
Sum1076
Variance1155.508
MonotonicityNot monotonic
2023-12-12T21:29:26.188247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
23 4
 
11.8%
22 3
 
8.8%
17 3
 
8.8%
21 2
 
5.9%
40 2
 
5.9%
69 1
 
2.9%
128 1
 
2.9%
4 1
 
2.9%
5 1
 
2.9%
28 1
 
2.9%
Other values (15) 15
44.1%
ValueCountFrequency (%)
1 1
 
2.9%
2 1
 
2.9%
3 1
 
2.9%
4 1
 
2.9%
5 1
 
2.9%
10 1
 
2.9%
12 1
 
2.9%
14 1
 
2.9%
17 3
8.8%
19 1
 
2.9%
ValueCountFrequency (%)
161 1
2.9%
128 1
2.9%
89 1
2.9%
69 1
2.9%
45 1
2.9%
43 1
2.9%
40 2
5.9%
31 1
2.9%
30 1
2.9%
28 1
2.9%

뇌질환 등
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean881.82353
Minimum27
Maximum4768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T21:29:26.429010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile92.8
Q1406.25
median653.5
Q3948.25
95-th percentile2632.6
Maximum4768
Range4741
Interquartile range (IQR)542

Descriptive statistics

Standard deviation916.98655
Coefficient of variation (CV)1.0398753
Kurtosis9.6579333
Mean881.82353
Median Absolute Deviation (MAD)282.5
Skewness2.8239297
Sum29982
Variance840864.33
MonotonicityNot monotonic
2023-12-12T21:29:26.634007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2812 1
 
2.9%
732 1
 
2.9%
704 1
 
2.9%
416 1
 
2.9%
793 1
 
2.9%
479 1
 
2.9%
925 1
 
2.9%
532 1
 
2.9%
403 1
 
2.9%
889 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
27 1
2.9%
72 1
2.9%
104 1
2.9%
217 1
2.9%
248 1
2.9%
291 1
2.9%
331 1
2.9%
360 1
2.9%
403 1
2.9%
416 1
2.9%
ValueCountFrequency (%)
4768 1
2.9%
2812 1
2.9%
2536 1
2.9%
1499 1
2.9%
1403 1
2.9%
1293 1
2.9%
1140 1
2.9%
1119 1
2.9%
956 1
2.9%
925 1
2.9%

중풍후유증
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7352941
Minimum0
Maximum29
Zeros2
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T21:29:26.823917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.65
Q14
median5.5
Q310
95-th percentile27.35
Maximum29
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.8559332
Coefficient of variation (CV)0.89933242
Kurtosis1.1769806
Mean8.7352941
Median Absolute Deviation (MAD)2.5
Skewness1.388658
Sum297
Variance61.715686
MonotonicityNot monotonic
2023-12-12T21:29:26.987140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
5 5
14.7%
4 5
14.7%
8 3
8.8%
3 2
 
5.9%
18 2
 
5.9%
10 2
 
5.9%
2 2
 
5.9%
17 2
 
5.9%
6 2
 
5.9%
0 2
 
5.9%
Other values (7) 7
20.6%
ValueCountFrequency (%)
0 2
 
5.9%
1 1
 
2.9%
2 2
 
5.9%
3 2
 
5.9%
4 5
14.7%
5 5
14.7%
6 2
 
5.9%
7 1
 
2.9%
8 3
8.8%
9 1
 
2.9%
ValueCountFrequency (%)
29 1
 
2.9%
28 1
 
2.9%
27 1
 
2.9%
18 2
5.9%
17 2
5.9%
15 1
 
2.9%
10 2
5.9%
9 1
 
2.9%
8 3
8.8%
7 1
 
2.9%

진전
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6470588
Minimum0
Maximum20
Zeros7
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T21:29:27.129450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35.75
95-th percentile15.4
Maximum20
Range20
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation5.0023168
Coefficient of variation (CV)1.0764479
Kurtosis2.7429001
Mean4.6470588
Median Absolute Deviation (MAD)2
Skewness1.7161161
Sum158
Variance25.023173
MonotonicityNot monotonic
2023-12-12T21:29:27.292554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 7
20.6%
0 7
20.6%
4 4
11.8%
2 4
11.8%
8 3
8.8%
5 2
 
5.9%
14 1
 
2.9%
13 1
 
2.9%
7 1
 
2.9%
20 1
 
2.9%
Other values (3) 3
8.8%
ValueCountFrequency (%)
0 7
20.6%
1 1
 
2.9%
2 4
11.8%
3 7
20.6%
4 4
11.8%
5 2
 
5.9%
6 1
 
2.9%
7 1
 
2.9%
8 3
8.8%
13 1
 
2.9%
ValueCountFrequency (%)
20 1
 
2.9%
18 1
 
2.9%
14 1
 
2.9%
13 1
 
2.9%
8 3
8.8%
7 1
 
2.9%
6 1
 
2.9%
5 2
 
5.9%
4 4
11.8%
3 7
20.6%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204.61765
Minimum4
Maximum769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T21:29:27.466283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile34.4
Q1103
median147
Q3201
95-th percentile659.95
Maximum769
Range765
Interquartile range (IQR)98

Descriptive statistics

Standard deviation192.56184
Coefficient of variation (CV)0.94108128
Kurtosis2.9965361
Mean204.61765
Median Absolute Deviation (MAD)52
Skewness1.9154299
Sum6957
Variance37080.061
MonotonicityNot monotonic
2023-12-12T21:29:27.667052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
123 2
 
5.9%
710 1
 
2.9%
169 1
 
2.9%
99 1
 
2.9%
156 1
 
2.9%
120 1
 
2.9%
170 1
 
2.9%
139 1
 
2.9%
131 1
 
2.9%
143 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
4 1
2.9%
11 1
2.9%
47 1
2.9%
48 1
2.9%
49 1
2.9%
51 1
2.9%
90 1
2.9%
94 1
2.9%
99 1
2.9%
115 1
2.9%
ValueCountFrequency (%)
769 1
2.9%
710 1
2.9%
633 1
2.9%
625 1
2.9%
307 1
2.9%
269 1
2.9%
268 1
2.9%
254 1
2.9%
202 1
2.9%
198 1
2.9%

Interactions

2023-12-12T21:29:22.908777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.427425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.172498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.862719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:20.554572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:21.524444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:22.204490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:23.008161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.531130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.279536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.965679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:20.658279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:21.626233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:22.311193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:23.132294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.639198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.363221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:20.051809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:20.749331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:21.711246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:22.403182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:23.269209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.765880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.460532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:20.142932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:21.167647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:21.805638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:22.492748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:23.501654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.855613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.565032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:20.229078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:21.252314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:21.916594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:22.577697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:23.679840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:18.948657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.649485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:20.323919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:21.343716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:22.002852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:22.676096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:23.890861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.055931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:19.746269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:20.436099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:21.434460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:22.112426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:29:22.789319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:29:28.136352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1구분2치매파킨슨병알츠하이머뇌질환 등중풍후유증진전기타
구분11.0000.0000.8650.8970.8240.2550.6850.8200.900
구분20.0001.0000.0000.0000.0000.5310.2190.0000.255
치매0.8650.0001.0000.8560.9100.9830.6490.8240.937
파킨슨병0.8970.0000.8561.0000.9810.8600.8270.8720.870
알츠하이머0.8240.0000.9100.9811.0000.8880.8510.8580.851
뇌질환 등0.2550.5310.9830.8600.8881.0000.5740.8300.918
중풍후유증0.6850.2190.6490.8270.8510.5741.0000.6520.719
진전0.8200.0000.8240.8720.8580.8300.6521.0000.754
기타0.9000.2550.9370.8700.8510.9180.7190.7541.000
2023-12-12T21:29:28.307964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
치매파킨슨병알츠하이머뇌질환 등중풍후유증진전기타구분2
치매1.0000.9040.8200.9380.8270.7970.9470.000
파킨슨병0.9041.0000.8560.8230.7700.7920.9300.000
알츠하이머0.8200.8561.0000.7280.6300.6540.8160.000
뇌질환 등0.9380.8230.7281.0000.8510.7330.9380.354
중풍후유증0.8270.7700.6300.8511.0000.7160.8330.202
진전0.7970.7920.6540.7330.7161.0000.7620.000
기타0.9470.9300.8160.9380.8330.7621.0000.158
구분20.0000.0000.0000.3540.2020.0000.1581.000

Missing values

2023-12-12T21:29:24.061300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:29:24.241928image/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

구분1구분2치매파킨슨병알츠하이머뇌질환 등중풍후유증진전기타
0서울남자9673066928122914710
1서울여자8133648914991713625
2부산남자425131301293157307
3부산여자3921813175583269
4대구남자32810221956183193
5대구여자2861141953493151
6인천남자501134431403278202
7인천여자37416745818174196
8광주남자158742250520128
9광주여자16471222915090
구분1구분2치매파킨슨병알츠하이머뇌질환 등중풍후유증진전기타
24전북남자3151092292541170
25전북여자3101022453253139
26전남남자306861473285169
27전남여자268912140354131
28경북남자514116231140105198
29경북여자3971392861148172
30경남남자46613640111984268
31경남여자4261554069673254
32제주남자951652481047
33제주여자651641040048