Overview

Dataset statistics

Number of variables7
Number of observations3498
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory205.1 KiB
Average record size in memory60.0 B

Variable types

Numeric4
Categorical2
Text1

Dataset

Description○ 내용: 해당년도 치매로 의료 이용이 있는 환자의 비율○ 대상: 해당년도 입원 또는 외래로 요양기관을 방문한 치매 환자○ 산출식- 분자: 치매로 입원 또는 외래 의료이용이 있는 실인원 수- 분모: 건강보험 가입자 또는 의료급여수급권자 수※ 치매 환자: 치매 상병 코드(F00~F03, G30)가 있고 치매 약제를 처방 받은 환자
Author국민건강보험공단
URLhttps://www.data.go.kr/data/15089354/fileData.do

Alerts

지표명 has constant value ""Constant
지표연도 is highly overall correlated with 분자(명) and 1 other fieldsHigh correlation
분모(명) is highly overall correlated with 분자(명) and 2 other fieldsHigh correlation
분자(명) is highly overall correlated with 지표연도 and 1 other fieldsHigh correlation
지표값(퍼센트) is highly overall correlated with 지표연도 and 1 other fieldsHigh correlation
시도 is highly overall correlated with 분모(명)High correlation

Reproduction

Analysis started2023-12-12 08:20:53.301141
Analysis finished2023-12-12 08:20:56.282191
Duration2.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지표연도
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.9971
Minimum2009
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2023-12-12T17:20:56.656106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12012
median2015
Q32018
95-th percentile2021
Maximum2021
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7363792
Coefficient of variation (CV)0.0018542851
Kurtosis-1.2109899
Mean2014.9971
Median Absolute Deviation (MAD)3
Skewness0.0023734661
Sum7048460
Variance13.960529
MonotonicityIncreasing
2023-12-12T17:20:56.785116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2010 271
 
7.7%
2014 271
 
7.7%
2016 271
 
7.7%
2012 270
 
7.7%
2013 270
 
7.7%
2015 270
 
7.7%
2018 269
 
7.7%
2011 268
 
7.7%
2017 268
 
7.7%
2019 268
 
7.7%
Other values (3) 802
22.9%
ValueCountFrequency (%)
2009 266
7.6%
2010 271
7.7%
2011 268
7.7%
2012 270
7.7%
2013 270
7.7%
2014 271
7.7%
2015 270
7.7%
2016 271
7.7%
2017 268
7.7%
2018 269
7.7%
ValueCountFrequency (%)
2021 268
7.7%
2020 268
7.7%
2019 268
7.7%
2018 269
7.7%
2017 268
7.7%
2016 271
7.7%
2015 270
7.7%
2014 271
7.7%
2013 270
7.7%
2012 270
7.7%

시도
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size27.5 KiB
경기도
577 
서울특별시
338 
경상북도
325 
경상남도
300 
전라남도
299 
Other values (13)
1659 

Length

Max length7
Median length5
Mean length4.1089194
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전국
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
경기도 577
16.5%
서울특별시 338
9.7%
경상북도 325
9.3%
경상남도 300
8.6%
전라남도 299
8.5%
강원도 247
7.1%
충청남도 225
 
6.4%
부산광역시 221
 
6.3%
전라북도 208
 
5.9%
충청북도 191
 
5.5%
Other values (8) 567
16.2%

Length

2023-12-12T17:20:56.940592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 577
16.5%
서울특별시 338
9.7%
경상북도 325
9.3%
경상남도 300
8.6%
전라남도 299
8.5%
강원도 247
7.1%
충청남도 225
 
6.4%
부산광역시 221
 
6.3%
전라북도 208
 
5.9%
충청북도 191
 
5.5%
Other values (8) 567
16.2%

시군구
Text

Distinct239
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size27.5 KiB
2023-12-12T17:20:57.377229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3724986
Min length2

Characters and Unicode

Total characters11797
Distinct characters145
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 row전체
2nd row전체
3rd row종로구
4th row중구
5th row용산구
ValueCountFrequency (%)
전체 231
 
5.9%
동구 78
 
2.0%
중구 78
 
2.0%
남구 75
 
1.9%
서구 65
 
1.7%
북구 65
 
1.7%
창원시 62
 
1.6%
수원시 52
 
1.3%
청주시 42
 
1.1%
성남시 39
 
1.0%
Other values (237) 3136
79.9%
2023-12-12T17:20:57.950761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 1388
 
11.8%
시 1298
 
11.0%
군 1123
 
9.5%
425
 
3.6%
천 316
 
2.7%
주 303
 
2.6%
산 298
 
2.5%
양 286
 
2.4%
성 272
 
2.3%
동 260
 
2.2%
Other values (135) 5828
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11372
96.4%
Space Separator 425
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1388
 
12.2%
시 1298
 
11.4%
군 1123
 
9.9%
천 316
 
2.8%
주 303
 
2.7%
산 298
 
2.6%
양 286
 
2.5%
성 272
 
2.4%
동 260
 
2.3%
전 257
 
2.3%
Other values (134) 5571
49.0%
Space Separator
ValueCountFrequency (%)
425
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11372
96.4%
Common 425
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1388
 
12.2%
시 1298
 
11.4%
군 1123
 
9.9%
천 316
 
2.8%
주 303
 
2.7%
산 298
 
2.6%
양 286
 
2.5%
성 272
 
2.4%
동 260
 
2.3%
전 257
 
2.3%
Other values (134) 5571
49.0%
Common
ValueCountFrequency (%)
425
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11372
96.4%
ASCII 425
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 1388
 
12.2%
시 1298
 
11.4%
군 1123
 
9.9%
천 316
 
2.8%
주 303
 
2.7%
산 298
 
2.6%
양 286
 
2.5%
성 272
 
2.4%
동 260
 
2.3%
전 257
 
2.3%
Other values (134) 5571
49.0%
ASCII
ValueCountFrequency (%)
425
100.0%

지표명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.5 KiB
치매의료이용률
3498 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
치매의료이용률 3498
100.0%

Length

2023-12-12T17:20:58.127184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:20:58.233742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
치매의료이용률 3498
100.0%

분모(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct3474
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567346.04
Minimum286
Maximum52029605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2023-12-12T17:20:58.438128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum286
5-th percentile26552.6
Q162375.5
median193441.5
Q3346144.25
95-th percentile1461721.5
Maximum52029605
Range52029319
Interquartile range (IQR)283768.75

Descriptive statistics

Standard deviation3250958.5
Coefficient of variation (CV)5.7301159
Kurtosis212.21753
Mean567346.04
Median Absolute Deviation (MAD)137166.5
Skewness14.093134
Sum1.9845764 × 109
Variance1.0568731 × 1013
MonotonicityNot monotonic
2023-12-12T17:20:58.642008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26109 2
 
0.1%
77341 2
 
0.1%
29311 2
 
0.1%
18286 2
 
0.1%
155484 2
 
0.1%
350296 2
 
0.1%
68669 2
 
0.1%
268323 2
 
0.1%
109862 2
 
0.1%
18349 2
 
0.1%
Other values (3464) 3478
99.4%
ValueCountFrequency (%)
286 1
< 0.1%
724 2
0.1%
897 1
< 0.1%
1088 1
< 0.1%
1209 1
< 0.1%
1221 1
< 0.1%
1326 1
< 0.1%
1449 1
< 0.1%
1732 1
< 0.1%
2201 1
< 0.1%
ValueCountFrequency (%)
52029605 1
< 0.1%
51984152 1
< 0.1%
51977034 1
< 0.1%
51810274 1
< 0.1%
51679758 1
< 0.1%
51519912 1
< 0.1%
51245804 1
< 0.1%
50957456 1
< 0.1%
50006972 1
< 0.1%
49826553 1
< 0.1%

분자(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct2658
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6987.7504
Minimum0
Maximum1029986
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2023-12-12T17:20:58.870371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile469.7
Q11240.25
median2215
Q33752.5
95-th percentile14135.35
Maximum1029986
Range1029986
Interquartile range (IQR)2512.25

Descriptive statistics

Standard deviation42723.703
Coefficient of variation (CV)6.1140855
Kurtosis342.96979
Mean6987.7504
Median Absolute Deviation (MAD)1157
Skewness17.421135
Sum24443151
Variance1.8253148 × 109
MonotonicityNot monotonic
2023-12-12T17:20:59.091568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.2%
1824 5
 
0.1%
2190 5
 
0.1%
860 5
 
0.1%
1070 4
 
0.1%
902 4
 
0.1%
1386 4
 
0.1%
2361 4
 
0.1%
2560 4
 
0.1%
2292 4
 
0.1%
Other values (2648) 3451
98.7%
ValueCountFrequency (%)
0 8
0.2%
1 1
 
< 0.1%
2 1
 
< 0.1%
6 2
 
0.1%
64 1
 
< 0.1%
81 1
 
< 0.1%
85 1
 
< 0.1%
99 2
 
0.1%
102 1
 
< 0.1%
103 1
 
< 0.1%
ValueCountFrequency (%)
1029986 1
< 0.1%
966440 1
< 0.1%
925734 1
< 0.1%
844765 1
< 0.1%
762298 1
< 0.1%
690143 1
< 0.1%
617999 1
< 0.1%
552977 1
< 0.1%
478002 1
< 0.1%
414791 1
< 0.1%

지표값(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct564
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7849914
Minimum0
Maximum9.6
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2023-12-12T17:20:59.272592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.43
Q10.84
median1.34
Q32.26
95-th percentile4.86
Maximum9.6
Range9.6
Interquartile range (IQR)1.42

Descriptive statistics

Standard deviation1.3793383
Coefficient of variation (CV)0.77274227
Kurtosis3.0152463
Mean1.7849914
Median Absolute Deviation (MAD)0.61
Skewness1.6897749
Sum6243.9
Variance1.9025742
MonotonicityNot monotonic
2023-12-12T17:20:59.472440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61 24
 
0.7%
1.0 24
 
0.7%
1.4 24
 
0.7%
0.64 24
 
0.7%
1.2 24
 
0.7%
0.84 24
 
0.7%
0.73 24
 
0.7%
1.34 24
 
0.7%
1.11 22
 
0.6%
1.27 22
 
0.6%
Other values (554) 3262
93.3%
ValueCountFrequency (%)
0.0 8
0.2%
0.11 1
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.21 1
 
< 0.1%
0.22 1
 
< 0.1%
0.23 2
 
0.1%
0.24 2
 
0.1%
0.25 4
0.1%
0.26 2
 
0.1%
ValueCountFrequency (%)
9.6 1
< 0.1%
9.13 1
< 0.1%
8.76 1
< 0.1%
8.28 1
< 0.1%
8.02 1
< 0.1%
7.68 1
< 0.1%
7.54 1
< 0.1%
7.45 1
< 0.1%
7.4 1
< 0.1%
7.35 1
< 0.1%

Interactions

2023-12-12T17:20:55.567609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:53.945768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:54.555295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:55.058718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:55.670536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:54.112054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:54.692188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:55.170206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:55.786255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:54.286327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:54.825299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:55.285333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:55.917195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:54.409780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:54.945675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:55.407154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:20:59.623182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지표연도시도분모(명)분자(명)지표값(퍼센트)
지표연도1.0000.0000.0000.0360.576
시도0.0001.0000.8060.6670.480
분모(명)0.0000.8061.0000.8510.000
분자(명)0.0360.6670.8511.0000.000
지표값(퍼센트)0.5760.4800.0000.0001.000
2023-12-12T17:20:59.748766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지표연도분모(명)분자(명)지표값(퍼센트)시도
지표연도1.0000.0160.5440.6240.000
분모(명)0.0161.0000.722-0.5430.589
분자(명)0.5440.7221.0000.1210.328
지표값(퍼센트)0.624-0.5430.1211.0000.206
시도0.0000.5890.3280.2061.000

Missing values

2023-12-12T17:20:56.074650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:20:56.208616image/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

지표연도시도시군구지표명분모(명)분자(명)지표값(퍼센트)
02009전국전체치매의료이용률497135222314110.47
12009서울특별시전체치매의료이용률10299410369600.36
22009서울특별시종로구치매의료이용률1754207470.43
32009서울특별시중구치매의료이용률1369196470.47
42009서울특별시용산구치매의료이용률24184010270.42
52009서울특별시성동구치매의료이용률31780112190.38
62009서울특별시광진구치매의료이용률37916511920.31
72009서울특별시동대문구치매의료이용률37408414650.39
82009서울특별시중랑구치매의료이용률43073014940.35
92009서울특별시성북구치매의료이용률47636918880.4
지표연도시도시군구지표명분모(명)분자(명)지표값(퍼센트)
34882021경상남도고성군치매의료이용률5122327895.44
34892021경상남도남해군치매의료이용률4275930007.02
34902021경상남도하동군치매의료이용률4424328696.48
34912021경상남도산청군치매의료이용률3463123966.92
34922021경상남도함양군치매의료이용률3871628057.25
34932021경상남도거창군치매의료이용률6102328944.74
34942021경상남도합천군치매의료이용률4350930166.93
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