Overview

Dataset statistics

Number of variables7
Number of observations3486
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory204.4 KiB
Average record size in memory60.0 B

Variable types

Numeric4
Categorical2
Text1

Dataset

Description○ 내용: 해당년도 근골격계질환 관련 의료 이용이 있는 환자의 비율○ 대상: 해당년도 직장가입자○ 산출식- 분자: 근골격계질환 관련 주상병코드(첫번째자리 M)로 의료 이용이 있는 환자 수- 분모: 직장가입자 수
Author국민건강보험공단
URLhttps://www.data.go.kr/data/15089375/fileData.do

Alerts

지표명 has constant value ""Constant
지표연도 is highly overall correlated with 지표값(퍼센트)High correlation
분모(명) is highly overall correlated with 분자(명)High correlation
분자(명) is highly overall correlated with 분모(명)High correlation
지표값(퍼센트) is highly overall correlated with 지표연도High correlation

Reproduction

Analysis started2023-12-11 23:09:36.864529
Analysis finished2023-12-11 23:09:38.903342
Duration2.04 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%
Mean2015.006
Minimum2009
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T08:09:38.969330image/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 deviation3.7367028
Coefficient of variation (CV)0.0018544375
Kurtosis-1.2104475
Mean2015.006
Median Absolute Deviation (MAD)3
Skewness-0.0011248425
Sum7024311
Variance13.962948
MonotonicityIncreasing
2023-12-12T08:09:39.100634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2015 270
 
7.7%
2016 270
 
7.7%
2013 269
 
7.7%
2014 269
 
7.7%
2011 268
 
7.7%
2012 268
 
7.7%
2017 268
 
7.7%
2018 268
 
7.7%
2019 268
 
7.7%
2020 268
 
7.7%
Other values (3) 800
22.9%
ValueCountFrequency (%)
2009 266
7.6%
2010 266
7.6%
2011 268
7.7%
2012 268
7.7%
2013 269
7.7%
2014 269
7.7%
2015 270
7.7%
2016 270
7.7%
2017 268
7.7%
2018 268
7.7%
ValueCountFrequency (%)
2021 268
7.7%
2020 268
7.7%
2019 268
7.7%
2018 268
7.7%
2017 268
7.7%
2016 270
7.7%
2015 270
7.7%
2014 269
7.7%
2013 269
7.7%
2012 268
7.7%

시도
Categorical

Distinct18
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
경기도
575 
서울특별시
338 
경상북도
325 
전라남도
299 
경상남도
295 
Other values (13)
1654 

Length

Max length7
Median length5
Mean length4.10786
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 575
16.5%
서울특별시 338
9.7%
경상북도 325
9.3%
전라남도 299
8.6%
경상남도 295
8.5%
강원도 247
7.1%
충청남도 225
 
6.5%
부산광역시 221
 
6.3%
전라북도 208
 
6.0%
충청북도 189
 
5.4%
Other values (8) 564
16.2%

Length

2023-12-12T08:09:39.217780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 575
16.5%
서울특별시 338
9.7%
경상북도 325
9.3%
전라남도 299
8.6%
경상남도 295
8.5%
강원도 247
7.1%
충청남도 225
 
6.5%
부산광역시 221
 
6.3%
전라북도 208
 
6.0%
충청북도 189
 
5.4%
Other values (8) 564
16.2%

시군구
Text

Distinct239
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
2023-12-12T08:09:39.565302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3646013
Min length2

Characters and Unicode

Total characters11729
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 230
 
5.9%
동구 78
 
2.0%
중구 78
 
2.0%
남구 75
 
1.9%
서구 65
 
1.7%
북구 65
 
1.7%
창원시 57
 
1.5%
수원시 52
 
1.3%
청주시 40
 
1.0%
고양시 39
 
1.0%
Other values (237) 3125
80.0%
2023-12-12T08:09:40.046747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 1380
 
11.8%
시 1288
 
11.0%
군 1123
 
9.6%
418
 
3.6%
천 315
 
2.7%
주 300
 
2.6%
산 295
 
2.5%
양 286
 
2.4%
성 271
 
2.3%
동 260
 
2.2%
Other values (135) 5793
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11311
96.4%
Space Separator 418
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1380
 
12.2%
시 1288
 
11.4%
군 1123
 
9.9%
천 315
 
2.8%
주 300
 
2.7%
산 295
 
2.6%
양 286
 
2.5%
성 271
 
2.4%
동 260
 
2.3%
전 256
 
2.3%
Other values (134) 5537
49.0%
Space Separator
ValueCountFrequency (%)
418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11311
96.4%
Common 418
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1380
 
12.2%
시 1288
 
11.4%
군 1123
 
9.9%
천 315
 
2.8%
주 300
 
2.7%
산 295
 
2.6%
양 286
 
2.5%
성 271
 
2.4%
동 260
 
2.3%
전 256
 
2.3%
Other values (134) 5537
49.0%
Common
ValueCountFrequency (%)
418
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11311
96.4%
ASCII 418
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 1380
 
12.2%
시 1288
 
11.4%
군 1123
 
9.9%
천 315
 
2.8%
주 300
 
2.7%
산 295
 
2.6%
양 286
 
2.5%
성 271
 
2.4%
동 260
 
2.3%
전 256
 
2.3%
Other values (134) 5537
49.0%
ASCII
ValueCountFrequency (%)
418
100.0%

지표명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
근골격계의료이용률
3486 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
근골격계의료이용률 3486
100.0%

Length

2023-12-12T08:09:40.180487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:09:40.270552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
근골격계의료이용률 3486
100.0%

분모(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct3416
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162150.92
Minimum1359
Maximum17273414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T08:09:40.404504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1359
5-th percentile5361.75
Q113893.5
median41592.5
Q379534.75
95-th percentile437919.75
Maximum17273414
Range17272055
Interquartile range (IQR)65641.25

Descriptive statistics

Standard deviation954657.81
Coefficient of variation (CV)5.8874646
Kurtosis208.06553
Mean162150.92
Median Absolute Deviation (MAD)30365
Skewness13.771639
Sum5.6525812 × 108
Variance9.1137154 × 1011
MonotonicityNot monotonic
2023-12-12T08:09:40.574946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6435 3
 
0.1%
47547 3
 
0.1%
6080 2
 
0.1%
5947 2
 
0.1%
10651 2
 
0.1%
15009 2
 
0.1%
67472 2
 
0.1%
87673 2
 
0.1%
141161 2
 
0.1%
45578 2
 
0.1%
Other values (3406) 3464
99.4%
ValueCountFrequency (%)
1359 1
< 0.1%
1537 2
0.1%
1617 1
< 0.1%
1625 1
< 0.1%
1654 1
< 0.1%
1666 1
< 0.1%
1699 1
< 0.1%
1739 1
< 0.1%
1851 1
< 0.1%
1865 1
< 0.1%
ValueCountFrequency (%)
17273414 1
< 0.1%
16995762 1
< 0.1%
16563389 1
< 0.1%
15940192 1
< 0.1%
15441440 1
< 0.1%
15006289 1
< 0.1%
14485092 1
< 0.1%
14259200 1
< 0.1%
13635522 1
< 0.1%
13094050 1
< 0.1%

분자(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct3329
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63318.147
Minimum376
Maximum7440584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T08:09:40.721626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum376
5-th percentile2354.75
Q16097.5
median16522.5
Q332391
95-th percentile172679
Maximum7440584
Range7440208
Interquartile range (IQR)26293.5

Descriptive statistics

Standard deviation376181.78
Coefficient of variation (CV)5.9411369
Kurtosis232.49976
Mean63318.147
Median Absolute Deviation (MAD)11775.5
Skewness14.470144
Sum2.2072706 × 108
Variance1.4151273 × 1011
MonotonicityNot monotonic
2023-12-12T08:09:40.851531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14906 3
 
0.1%
33303 3
 
0.1%
3261 3
 
0.1%
3263 3
 
0.1%
4930 2
 
0.1%
3742 2
 
0.1%
16539 2
 
0.1%
2215 2
 
0.1%
2679 2
 
0.1%
35850 2
 
0.1%
Other values (3319) 3462
99.3%
ValueCountFrequency (%)
376 1
< 0.1%
478 1
< 0.1%
593 1
< 0.1%
618 1
< 0.1%
652 1
< 0.1%
655 1
< 0.1%
672 1
< 0.1%
707 1
< 0.1%
718 1
< 0.1%
741 1
< 0.1%
ValueCountFrequency (%)
7440584 1
< 0.1%
7042014 1
< 0.1%
6999865 1
< 0.1%
6630709 1
< 0.1%
6326270 1
< 0.1%
6049618 1
< 0.1%
5679130 1
< 0.1%
5568398 1
< 0.1%
5207382 1
< 0.1%
4901099 1
< 0.1%

지표값(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct1719
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.472593
Minimum21.42
Maximum55.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T08:09:40.978117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.42
5-th percentile29.3225
Q138.34
median41.89
Q345.0975
95-th percentile50.48
Maximum55.55
Range34.13
Interquartile range (IQR)6.7575

Descriptive statistics

Standard deviation5.8394619
Coefficient of variation (CV)0.14080291
Kurtosis0.84965335
Mean41.472593
Median Absolute Deviation (MAD)3.355
Skewness-0.63295475
Sum144573.46
Variance34.099316
MonotonicityNot monotonic
2023-12-12T08:09:41.114248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.05 9
 
0.3%
41.92 8
 
0.2%
44.62 8
 
0.2%
43.22 7
 
0.2%
40.89 7
 
0.2%
42.49 7
 
0.2%
40.78 7
 
0.2%
42.75 7
 
0.2%
42.1 7
 
0.2%
41.87 7
 
0.2%
Other values (1709) 3412
97.9%
ValueCountFrequency (%)
21.42 1
< 0.1%
21.55 1
< 0.1%
21.82 1
< 0.1%
22.12 2
0.1%
22.21 1
< 0.1%
22.42 1
< 0.1%
22.48 1
< 0.1%
22.6 1
< 0.1%
22.74 1
< 0.1%
22.75 1
< 0.1%
ValueCountFrequency (%)
55.55 1
< 0.1%
55.28 1
< 0.1%
54.87 1
< 0.1%
54.84 1
< 0.1%
54.73 1
< 0.1%
54.39 1
< 0.1%
54.36 1
< 0.1%
54.23 1
< 0.1%
54.14 1
< 0.1%
53.96 1
< 0.1%

Interactions

2023-12-12T08:09:38.202036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.204289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.499168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.814267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:38.312029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.272474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.572361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.912581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:38.474982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.349505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.655182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.996971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:38.594397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.423231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:37.733801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:09:38.091809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:09:41.221349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지표연도시도분모(명)분자(명)지표값(퍼센트)
지표연도1.0000.0000.0000.0000.393
시도0.0001.0000.7050.7520.521
분모(명)0.0000.7051.0000.9630.086
분자(명)0.0000.7520.9631.0000.091
지표값(퍼센트)0.3930.5210.0860.0911.000
2023-12-12T08:09:41.329702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지표연도분모(명)분자(명)지표값(퍼센트)시도
지표연도1.0000.1220.2020.5780.000
분모(명)0.1221.0000.992-0.4270.410
분자(명)0.2020.9921.000-0.3330.352
지표값(퍼센트)0.578-0.427-0.3331.0000.228
시도0.0000.4100.3520.2281.000

Missing values

2023-12-12T08:09:38.727235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:09:38.853340image/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)
02009전국전체근골격계의료이용률11414267293905725.75
12009서울특별시전체근골격계의료이용률395743292277723.32
22009서울특별시종로구근골격계의료이용률2427785486722.6
32009서울특별시중구근골격계의료이용률45841610141122.12
42009서울특별시용산구근골격계의료이용률1185372796523.59
52009서울특별시성동구근골격계의료이용률1003232499524.91
62009서울특별시광진구근골격계의료이용률634031621825.58
72009서울특별시동대문구근골격계의료이용률776532026326.09
82009서울특별시중랑구근골격계의료이용률372631041627.95
92009서울특별시성북구근골격계의료이용률463371217526.27
지표연도시도시군구지표명분모(명)분자(명)지표값(퍼센트)
34762021경상남도고성군근골격계의료이용률15038754650.18
34772021경상남도남해군근골격계의료이용률8207432452.69
34782021경상남도하동군근골격계의료이용률8608442451.39
34792021경상남도산청군근골격계의료이용률8509455253.5
34802021경상남도함양군근골격계의료이용률8520445152.24
34812021경상남도거창군근골격계의료이용률12766681753.4
34822021경상남도합천군근골격계의료이용률8968450750.26
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