Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory830.1 KiB
Average record size in memory85.0 B

Variable types

Categorical3
Text1
Numeric5

Dataset

Description2021년 질병코드 대분류별 입원 진료 현황1. 수진기준(한의분류 제외, 약국제외)2. 건강보험 급여실적(의료급여 제외)이며, 비급여는 제외- 진료기간: 2010년~2021년 (2022년 6월 지급분까지 반영)3. 아래 질병통계 자료는 요양기관에서 환자진료중 진단명이 확정되지 않은 상태에서의 호소,증세 등에 따라일차진단명을 부여하고 청구한 내역중 주진단명 기준으로 발췌한 것이므로 최종확정된 질병과는 다를수 있음-주상병코드: 표 참조(한국표준질병사인분류의 대분류별)* 상병중분류(298분류코드), 상병소분류별 등 더 세분화된 분류의 자료는 그 양이 방대하여 전산매체로 제공 불가하오니 양해하여주시기 바랍니다.※ 개인정보 식별 위험이 있는 경우, 해당 지역의 데이터값 제외 후 제공
Author국민건강보험공단
URLhttps://www.data.go.kr/data/15103872/fileData.do

Alerts

진료실인원(명) is highly overall correlated with 입내원일수(일) and 3 other fieldsHigh correlation
입내원일수(일) is highly overall correlated with 진료실인원(명) and 3 other fieldsHigh correlation
급여일수(일) is highly overall correlated with 진료실인원(명) and 3 other fieldsHigh correlation
진료비(천원) is highly overall correlated with 진료실인원(명) and 3 other fieldsHigh correlation
급여비(천원) is highly overall correlated with 진료실인원(명) and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 14:47:01.212907
Analysis finished2023-12-12 14:47:04.761929
Duration3.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

진료년도
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2013년
868 
2014년
865 
2021년
854 
2011년
849 
2012년
846 
Other values (7)
5718 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021년
2nd row2015년
3rd row2017년
4th row2017년
5th row2011년

Common Values

ValueCountFrequency (%)
2013년 868
8.7%
2014년 865
8.6%
2021년 854
8.5%
2011년 849
8.5%
2012년 846
8.5%
2015년 845
8.5%
2020년 833
8.3%
2010년 828
8.3%
2019년 821
8.2%
2016년 806
8.1%
Other values (2) 1585
15.8%

Length

2023-12-12T23:47:04.833792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013년 868
8.7%
2014년 865
8.6%
2021년 854
8.5%
2011년 849
8.5%
2012년 846
8.5%
2015년 845
8.5%
2020년 833
8.3%
2010년 828
8.3%
2019년 821
8.2%
2016년 806
8.1%
Other values (2) 1585
15.8%

주상병대분류
Categorical

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
신경계의 질환[G00-G99]
 
520
특정감염성 및 기생충성 질환[A00-B99]
 
516
임신, 출산 및 산욕[O00-O99]
 
514
순환기계의 질환[I00-I99]
 
512
신생물[C00-D48]
 
501
Other values (16)
7437 

Length

Max length42
Median length24
Mean length22.8431
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row특정감염성 및 기생충성 질환[A00-B99]
2nd row손상, 중독및 외인에 의한 특정 기타 결과[S00-T98]
3rd row달리 분류되지 않은 증상, 징후와 임상 및 검사의 이상 소견[R00-R99]
4th row눈및눈부속기의 질환[H00-H59]
5th row순환기계의 질환[I00-I99]

Common Values

ValueCountFrequency (%)
신경계의 질환[G00-G99] 520
 
5.2%
특정감염성 및 기생충성 질환[A00-B99] 516
 
5.2%
임신, 출산 및 산욕[O00-O99] 514
 
5.1%
순환기계의 질환[I00-I99] 512
 
5.1%
신생물[C00-D48] 501
 
5.0%
피부 및 피하조직의 질환[L00-L99] 498
 
5.0%
정신 및 행동장애[F00-F99] 493
 
4.9%
호흡기계의 질환[J00-J99] 487
 
4.9%
눈및눈부속기의 질환[H00-H59] 487
 
4.9%
건강상태 및 보건서비스 접촉에 영향을 주는 요인[Z00-Z99] 479
 
4.8%
Other values (11) 4993
49.9%

Length

2023-12-12T23:47:04.949450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 5290
 
13.7%
특정 945
 
2.5%
질환[g00-g99 520
 
1.4%
신경계의 520
 
1.4%
기생충성 516
 
1.3%
질환[a00-b99 516
 
1.3%
특정감염성 516
 
1.3%
임신 514
 
1.3%
출산 514
 
1.3%
산욕[o00-o99 514
 
1.3%
Other values (60) 28150
73.1%

시도
Categorical

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
1859 
서울특별시
991 
경상북도
947 
경상남도
914 
전라남도
820 
Other values (13)
4469 

Length

Max length7
Median length5
Mean length4.0747
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row충청북도
3rd row서울특별시
4th row울산광역시
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 1859
18.6%
서울특별시 991
9.9%
경상북도 947
9.5%
경상남도 914
9.1%
전라남도 820
8.2%
충청남도 720
 
7.2%
부산광역시 671
 
6.7%
강원도 600
 
6.0%
전라북도 572
 
5.7%
충청북도 506
 
5.1%
Other values (8) 1400
14.0%

Length

2023-12-12T23:47:05.104314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1859
18.6%
서울특별시 991
9.9%
경상북도 947
9.5%
경상남도 914
9.1%
전라남도 820
8.2%
충청남도 720
 
7.2%
부산광역시 671
 
6.7%
강원도 600
 
6.0%
전라북도 572
 
5.7%
충청북도 506
 
5.1%
Other values (8) 1400
14.0%

시군구
Text

Distinct248
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T23:47:05.497808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.4887
Min length1

Characters and Unicode

Total characters34887
Distinct characters144
Distinct categories3 ?
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 row성남시 수정구
2nd row진천군
3rd row도봉구
4th row북구
5th row양주시
ValueCountFrequency (%)
중구 270
 
2.4%
남구 229
 
2.0%
창원시 220
 
1.9%
동구 212
 
1.9%
북구 202
 
1.8%
서구 187
 
1.7%
용인시 146
 
1.3%
수원시 141
 
1.2%
부천시 134
 
1.2%
청주시 131
 
1.2%
Other values (243) 9441
83.5%
2023-12-12T23:47:06.079614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 4249
 
12.2%
시 4062
 
11.6%
군 3364
 
9.6%
1313
 
3.8%
산 981
 
2.8%
천 975
 
2.8%
주 962
 
2.8%
양 881
 
2.5%
성 817
 
2.3%
동 737
 
2.1%
Other values (134) 16546
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33533
96.1%
Space Separator 1313
 
3.8%
Connector Punctuation 41
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4249
 
12.7%
시 4062
 
12.1%
군 3364
 
10.0%
산 981
 
2.9%
천 975
 
2.9%
주 962
 
2.9%
양 881
 
2.6%
성 817
 
2.4%
동 737
 
2.2%
원 734
 
2.2%
Other values (132) 15771
47.0%
Space Separator
ValueCountFrequency (%)
1313
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33533
96.1%
Common 1354
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4249
 
12.7%
시 4062
 
12.1%
군 3364
 
10.0%
산 981
 
2.9%
천 975
 
2.9%
주 962
 
2.9%
양 881
 
2.6%
성 817
 
2.4%
동 737
 
2.2%
원 734
 
2.2%
Other values (132) 15771
47.0%
Common
ValueCountFrequency (%)
1313
97.0%
_ 41
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33533
96.1%
ASCII 1354
 
3.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 4249
 
12.7%
시 4062
 
12.1%
군 3364
 
10.0%
산 981
 
2.9%
천 975
 
2.9%
주 962
 
2.9%
양 881
 
2.6%
성 817
 
2.4%
동 737
 
2.2%
원 734
 
2.2%
Other values (132) 15771
47.0%
ASCII
ValueCountFrequency (%)
1313
97.0%
_ 41
 
3.0%

진료실인원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct8432
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33784.031
Minimum5
Maximum520574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:47:06.271679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile70
Q12846.25
median13750
Q342573.75
95-th percentile134859.05
Maximum520574
Range520569
Interquartile range (IQR)39727.5

Descriptive statistics

Standard deviation51731.696
Coefficient of variation (CV)1.531247
Kurtosis12.088856
Mean33784.031
Median Absolute Deviation (MAD)12952.5
Skewness2.9928195
Sum3.3784031 × 108
Variance2.6761683 × 109
MonotonicityNot monotonic
2023-12-12T23:47:06.417610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 34
 
0.3%
5 33
 
0.3%
7 23
 
0.2%
8 21
 
0.2%
17 17
 
0.2%
9 16
 
0.2%
10 15
 
0.1%
12 15
 
0.1%
16 14
 
0.1%
26 14
 
0.1%
Other values (8422) 9798
98.0%
ValueCountFrequency (%)
5 33
0.3%
6 34
0.3%
7 23
0.2%
8 21
0.2%
9 16
0.2%
10 15
0.1%
11 14
0.1%
12 15
0.1%
13 14
0.1%
14 4
 
< 0.1%
ValueCountFrequency (%)
520574 1
< 0.1%
510340 1
< 0.1%
507937 1
< 0.1%
446256 1
< 0.1%
436248 1
< 0.1%
428364 1
< 0.1%
417578 1
< 0.1%
398019 1
< 0.1%
392766 1
< 0.1%
390195 1
< 0.1%

입내원일수(일)
Real number (ℝ)

HIGH CORRELATION 

Distinct9416
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162791.5
Minimum0
Maximum2837770
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:47:06.568156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile355
Q111936.75
median71124
Q3201938.75
95-th percentile667885.85
Maximum2837770
Range2837770
Interquartile range (IQR)190002

Descriptive statistics

Standard deviation255607.95
Coefficient of variation (CV)1.5701553
Kurtosis14.707891
Mean162791.5
Median Absolute Deviation (MAD)67123
Skewness3.2804546
Sum1.627915 × 109
Variance6.5335422 × 1010
MonotonicityNot monotonic
2023-12-12T23:47:07.057631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 9
 
0.1%
13 8
 
0.1%
10 7
 
0.1%
82 7
 
0.1%
8 6
 
0.1%
64 6
 
0.1%
47 6
 
0.1%
24 6
 
0.1%
32 5
 
0.1%
23 5
 
0.1%
Other values (9406) 9935
99.4%
ValueCountFrequency (%)
0 3
< 0.1%
1 1
 
< 0.1%
2 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 3
< 0.1%
7 2
 
< 0.1%
8 6
0.1%
9 3
< 0.1%
10 7
0.1%
ValueCountFrequency (%)
2837770 1
< 0.1%
2451150 1
< 0.1%
2407733 1
< 0.1%
2375962 1
< 0.1%
2303244 1
< 0.1%
2220936 1
< 0.1%
2198748 1
< 0.1%
2162305 1
< 0.1%
2115587 1
< 0.1%
2096315 1
< 0.1%

급여일수(일)
Real number (ℝ)

HIGH CORRELATION 

Distinct9567
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245376.04
Minimum0
Maximum3425890
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:47:07.230741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile509.75
Q117994.75
median108449
Q3312017.75
95-th percentile998567.85
Maximum3425890
Range3425890
Interquartile range (IQR)294023

Descriptive statistics

Standard deviation360181.06
Coefficient of variation (CV)1.4678738
Kurtosis10.229577
Mean245376.04
Median Absolute Deviation (MAD)103036
Skewness2.7545708
Sum2.4537604 × 109
Variance1.297304 × 1011
MonotonicityNot monotonic
2023-12-12T23:47:07.382810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 7
 
0.1%
10 6
 
0.1%
23 6
 
0.1%
19 5
 
0.1%
12 5
 
0.1%
28 5
 
0.1%
265 5
 
0.1%
401 5
 
0.1%
32 5
 
0.1%
14 5
 
0.1%
Other values (9557) 9946
99.5%
ValueCountFrequency (%)
0 3
< 0.1%
1 1
 
< 0.1%
2 2
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 4
< 0.1%
9 3
< 0.1%
ValueCountFrequency (%)
3425890 1
< 0.1%
3322796 1
< 0.1%
3268334 1
< 0.1%
3235828 1
< 0.1%
3016299 1
< 0.1%
2979508 1
< 0.1%
2909542 1
< 0.1%
2867695 1
< 0.1%
2827519 1
< 0.1%
2683427 1
< 0.1%

진료비(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct9983
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8905457.5
Minimum80
Maximum1.6187581 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:47:07.573307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile35897.35
Q11159304.2
median4053990.5
Q311436541
95-th percentile33731860
Maximum1.6187581 × 108
Range1.6187573 × 108
Interquartile range (IQR)10277236

Descriptive statistics

Standard deviation12463719
Coefficient of variation (CV)1.3995596
Kurtosis11.495454
Mean8905457.5
Median Absolute Deviation (MAD)3552783
Skewness2.7764268
Sum8.9054575 × 1010
Variance1.5534428 × 1014
MonotonicityNot monotonic
2023-12-12T23:47:07.750895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242 3
 
< 0.1%
97827 2
 
< 0.1%
209700 2
 
< 0.1%
418088 2
 
< 0.1%
16865 2
 
< 0.1%
803 2
 
< 0.1%
402 2
 
< 0.1%
1537213 2
 
< 0.1%
3275 2
 
< 0.1%
262 2
 
< 0.1%
Other values (9973) 9979
99.8%
ValueCountFrequency (%)
80 1
< 0.1%
81 1
< 0.1%
85 1
< 0.1%
110 1
< 0.1%
111 2
< 0.1%
123 1
< 0.1%
134 1
< 0.1%
140 1
< 0.1%
147 1
< 0.1%
148 1
< 0.1%
ValueCountFrequency (%)
161875806 1
< 0.1%
131133326 1
< 0.1%
110250723 1
< 0.1%
104669251 1
< 0.1%
100833266 1
< 0.1%
95837913 1
< 0.1%
95223087 1
< 0.1%
94951023 1
< 0.1%
93978530 1
< 0.1%
92439220 1
< 0.1%

급여비(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct9984
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6747270.4
Minimum52
Maximum1.1368027 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:47:07.933215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile28788.7
Q1892626.75
median3057547.5
Q38526762.2
95-th percentile25848684
Maximum1.1368027 × 108
Range1.1368022 × 108
Interquartile range (IQR)7634135.5

Descriptive statistics

Standard deviation9538417.9
Coefficient of variation (CV)1.4136706
Kurtosis11.284292
Mean6747270.4
Median Absolute Deviation (MAD)2650868.5
Skewness2.8102583
Sum6.7472704 × 1010
Variance9.0981416 × 1013
MonotonicityNot monotonic
2023-12-12T23:47:08.095585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136 2
 
< 0.1%
539 2
 
< 0.1%
750 2
 
< 0.1%
3924 2
 
< 0.1%
8596 2
 
< 0.1%
1847 2
 
< 0.1%
34637 2
 
< 0.1%
12469 2
 
< 0.1%
11216 2
 
< 0.1%
746 2
 
< 0.1%
Other values (9974) 9980
99.8%
ValueCountFrequency (%)
52 1
< 0.1%
54 1
< 0.1%
57 1
< 0.1%
58 1
< 0.1%
65 1
< 0.1%
73 1
< 0.1%
78 1
< 0.1%
95 1
< 0.1%
99 1
< 0.1%
104 1
< 0.1%
ValueCountFrequency (%)
113680269 1
< 0.1%
93578388 1
< 0.1%
83258777 1
< 0.1%
82192496 1
< 0.1%
81928900 1
< 0.1%
77700669 1
< 0.1%
74996300 1
< 0.1%
72567652 1
< 0.1%
72439927 1
< 0.1%
72000454 1
< 0.1%

Interactions

2023-12-12T23:47:03.974096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.120271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.533370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.985564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:03.481536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:04.054009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.193132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.623284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:03.101774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:03.577556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:04.140315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.269940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.708456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:03.190428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:03.677293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:04.241386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.352278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.801476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:03.286207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:03.778945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:04.355360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.448880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:02.900445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:03.390150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:47:03.888850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:47:08.211600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
진료년도주상병대분류시도진료실인원(명)입내원일수(일)급여일수(일)진료비(천원)급여비(천원)
진료년도1.0000.0590.0380.0180.0260.0510.1480.141
주상병대분류0.0591.0000.0000.5600.5730.5800.4910.501
시도0.0380.0001.0000.2670.2270.2240.2790.233
진료실인원(명)0.0180.5600.2671.0000.9170.7900.6730.785
입내원일수(일)0.0260.5730.2270.9171.0000.9300.6250.737
급여일수(일)0.0510.5800.2240.7900.9301.0000.5460.673
진료비(천원)0.1480.4910.2790.6730.6250.5461.0000.948
급여비(천원)0.1410.5010.2330.7850.7370.6730.9481.000
2023-12-12T23:47:08.335191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
진료년도주상병대분류시도
진료년도1.0000.0200.013
주상병대분류0.0201.0000.000
시도0.0130.0001.000
2023-12-12T23:47:08.434415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
진료실인원(명)입내원일수(일)급여일수(일)진료비(천원)급여비(천원)진료년도주상병대분류시도
진료실인원(명)1.0000.9480.8790.8290.8030.0080.2430.105
입내원일수(일)0.9481.0000.9730.9200.9010.0110.2510.089
급여일수(일)0.8790.9731.0000.9170.9000.0210.2560.087
진료비(천원)0.8290.9200.9171.0000.9980.0630.2130.096
급여비(천원)0.8030.9010.9000.9981.0000.0590.2100.091
진료년도0.0080.0110.0210.0630.0591.0000.0200.013
주상병대분류0.2430.2510.2560.2130.2100.0201.0000.000
시도0.1050.0890.0870.0960.0910.0130.0001.000

Missing values

2023-12-12T23:47:04.494664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:47:04.693745image/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

진료년도주상병대분류시도시군구진료실인원(명)입내원일수(일)급여일수(일)진료비(천원)급여비(천원)
597662021년특정감염성 및 기생충성 질환[A00-B99]경기도성남시 수정구4266310942615966179915516081675
323782015년손상, 중독및 외인에 의한 특정 기타 결과[S00-T98]충청북도진천군2228510924113018062992554556857
428702017년달리 분류되지 않은 증상, 징후와 임상 및 검사의 이상 소견[R00-R99]서울특별시도봉구5484712294117441083103065167523
400402017년눈및눈부속기의 질환[H00-H59]울산광역시북구5758413084013812155816313928749
76632011년순환기계의 질환[I00-I99]경기도양주시33587250637547031118553379189193
563432020년순환기계의 질환[I00-I99]서울특별시마포구607354299855730103787311529666469
122492012년정신 및 행동장애[F00-F99]경상남도양산시1301525196110700551426697010511986
31322010년근골격계 및 결합조직의 질환[M00-M99]서울특별시성동구857646683497743272162690915544007
265502014년달리 분류되지 않은 증상, 징후와 임상 및 검사의 이상 소견[R00-R99]인천광역시강화군80081758539605914039555351
622472021년호흡기계의 질환[J00-J99]경상북도상주시3325615000818763494337107364819
진료년도주상병대분류시도시군구진료실인원(명)입내원일수(일)급여일수(일)진료비(천원)급여비(천원)
483202018년달리 분류되지 않은 증상, 징후와 임상 및 검사의 이상 소견[R00-R99]충청남도태안군13218316194702423068631460007
166042013년특정감염성 및 기생충성 질환[A00-B99]강원도삼척시13794369415133517344331298488
393912017년내분비, 영양 및 대사질환[E00-E90]경상북도포항시 북구3369419231127381172795125003585
608282021년정신 및 행동장애[F00-F99]경기도의왕시11358134774872734100133347467163
179742013년신경계의 질환[G00-G99]전라북도전주시 완산구22347166946269729107132937956010
444322018년혈액 및 조혈기관의 질환과 면역기전을 침범한 특정 장애[D50-D89]전라남도고흥군68219292798440869361499
519172019년피부 및 피하조직의 질환[L00-L99]광주광역시북구12564236149739776098616116922569
28162010년소화기계의 질환[K00-K93]경상북도구미시2087667666868953662596043518296924
250502014년피부 및 피하조직의 질환[L00-L99]강원도양양군78272191224254394663284966
585662020년선천성기형, 변형 및 염색체 이상[Q00-Q99]충청남도청양군125367487117069100127