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 고혈압 환자의 비율○ 대상: 해당년도 국민 건강보험 가입자 또는 의료급여수급권자○ 산출식- 분자: 고혈압 환자 수- 분모: 건강보험 가입자 또는 의료급여수급권자 수※ 고혈압 환자: 고혈압 주상병 코드(I10~I15)가 있고 고혈압 약제를 처방 받은 환자
Author국민건강보험공단
URLhttps://www.data.go.kr/data/15064607/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 17:38:04.475946
Analysis finished2023-12-12 17:38:06.368390
Duration1.89 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지표연도
Real number (ℝ)

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
2023-12-13T02:38:06.440885image/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
2023-12-13T02:38:06.593099image/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

2023-12-13T02:38:06.699490image/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
2023-12-13T02:38:06.983349image/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%
2023-12-13T02:38:07.452111image/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
고혈압의료이용률
3498 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고혈압의료이용률 3498
100.0%

Length

2023-12-13T02:38:07.596356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:38:07.698668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고혈압의료이용률 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
2023-12-13T02:38:08.134254image/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
2023-12-13T02:38:08.310863image/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 

Distinct3414
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92475.25
Minimum2
Maximum10583984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2023-12-13T02:38:08.552687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6840.1
Q114454.75
median30223.5
Q352333.25
95-th percentile227234.1
Maximum10583984
Range10583982
Interquartile range (IQR)37878.5

Descriptive statistics

Standard deviation533654.25
Coefficient of variation (CV)5.7707792
Kurtosis236.61902
Mean92475.25
Median Absolute Deviation (MAD)17578
Skewness14.736631
Sum3.2347843 × 108
Variance2.8478686 × 1011
MonotonicityNot monotonic
2023-12-13T02:38:08.752108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20823 3
 
0.1%
15598 2
 
0.1%
42328 2
 
0.1%
50933 2
 
0.1%
23622 2
 
0.1%
4959 2
 
0.1%
19458 2
 
0.1%
22339 2
 
0.1%
18413 2
 
0.1%
9789 2
 
0.1%
Other values (3404) 3477
99.4%
ValueCountFrequency (%)
2 1
< 0.1%
5 1
< 0.1%
6 2
0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
52 1
< 0.1%
ValueCountFrequency (%)
10583984 1
< 0.1%
10078489 1
< 0.1%
9692497 1
< 0.1%
9217494 1
< 0.1%
8824912 1
< 0.1%
8475742 1
< 0.1%
8111271 1
< 0.1%
7872637 1
< 0.1%
7569627 1
< 0.1%
7305116 1
< 0.1%

지표값(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct1739
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.130006
Minimum0.18
Maximum38.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.9 KiB
2023-12-13T02:38:08.949051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile11.37
Q114.64
median18.055
Q323.0375
95-th percentile30.1415
Maximum38.55
Range38.37
Interquartile range (IQR)8.3975

Descriptive statistics

Standard deviation5.863624
Coefficient of variation (CV)0.30651449
Kurtosis-0.12907504
Mean19.130006
Median Absolute Deviation (MAD)3.96
Skewness0.51701958
Sum66916.76
Variance34.382087
MonotonicityNot monotonic
2023-12-13T02:38:09.103327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.99 11
 
0.3%
17.19 9
 
0.3%
15.22 9
 
0.3%
15.36 7
 
0.2%
14.5 7
 
0.2%
15.45 7
 
0.2%
21.71 7
 
0.2%
16.79 7
 
0.2%
25.29 7
 
0.2%
13.8 6
 
0.2%
Other values (1729) 3421
97.8%
ValueCountFrequency (%)
0.18 1
< 0.1%
0.48 1
< 0.1%
0.64 1
< 0.1%
0.66 1
< 0.1%
0.82 1
< 0.1%
0.83 2
0.1%
0.9 1
< 0.1%
1.0 1
< 0.1%
1.75 1
< 0.1%
2.36 1
< 0.1%
ValueCountFrequency (%)
38.55 1
< 0.1%
37.49 1
< 0.1%
37.35 1
< 0.1%
36.87 1
< 0.1%
36.75 1
< 0.1%
36.45 1
< 0.1%
36.32 1
< 0.1%
36.3 1
< 0.1%
35.82 1
< 0.1%
35.56 1
< 0.1%

Interactions

2023-12-13T02:38:05.855042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:04.851999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.186805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.532993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.934548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:04.931498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.264810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.610268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:06.014951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.029475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.356349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.685471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:06.101020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.105823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.450357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:38:05.759745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:38:09.232294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지표연도시도분모(명)분자(명)지표값(퍼센트)
지표연도1.0000.0000.0000.0000.497
시도0.0001.0000.8060.7040.478
분모(명)0.0000.8061.0000.8970.038
분자(명)0.0000.7040.8971.0000.049
지표값(퍼센트)0.4970.4780.0380.0491.000
2023-12-13T02:38:09.360111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지표연도분모(명)분자(명)지표값(퍼센트)시도
지표연도1.0000.0160.1680.4360.000
분모(명)0.0161.0000.970-0.6810.589
분자(명)0.1680.9701.000-0.5210.409
지표값(퍼센트)0.436-0.681-0.5211.0000.204
시도0.0000.5890.4090.2041.000

Missing values

2023-12-13T02:38:06.223909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:38:06.326146image/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)
02009전국전체고혈압의료이용률49713522646969713.01
12009서울특별시전체고혈압의료이용률10299410131629412.78
22009서울특별시종로구고혈압의료이용률1754202417913.78
32009서울특별시중구고혈압의료이용률1369191961714.33
42009서울특별시용산구고혈압의료이용률2418403371313.94
52009서울특별시성동구고혈압의료이용률3178014232513.32
62009서울특별시광진구고혈압의료이용률3791654431511.69
72009서울특별시동대문구고혈압의료이용률3740845307114.19
82009서울특별시중랑구고혈압의료이용률4307305823313.52
92009서울특별시성북구고혈압의료이용률4763696600813.86
지표연도시도시군구지표명분모(명)분자(명)지표값(퍼센트)
34882021경상남도고성군고혈압의료이용률512231516329.6
34892021경상남도남해군고혈압의료이용률427591518235.51
34902021경상남도하동군고혈압의료이용률442431354730.62
34912021경상남도산청군고혈압의료이용률346311053330.41
34922021경상남도함양군고혈압의료이용률387161203331.08
34932021경상남도거창군고혈압의료이용률610231586526.0
34942021경상남도합천군고혈압의료이용률435091445133.21
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