Overview

Dataset statistics

Number of variables8
Number of observations725
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.7 KiB
Average record size in memory70.2 B

Variable types

Categorical2
Text1
Numeric5

Dataset

Description병원급 이상 의료기관의 진료과목별 시도별 진료비 통계 / 진료일자 기준(심사분은 각 진료년+4개월) (예) 진료년월: 2022.1월~12월, 심사년월: 2022.1월~2023.4월 / 보험자: 건강보험
URLhttps://www.data.go.kr/data/15055565/fileData.do

Alerts

진료년도 has constant value ""Constant
환자수 is highly overall correlated with 명세서청구건수 and 3 other fieldsHigh correlation
명세서청구건수 is highly overall correlated with 환자수 and 3 other fieldsHigh correlation
입내원일수 is highly overall correlated with 환자수 and 3 other fieldsHigh correlation
요양급여비용총액 is highly overall correlated with 환자수 and 3 other fieldsHigh correlation
보험자부담금 is highly overall correlated with 환자수 and 3 other fieldsHigh correlation
요양급여비용총액 has unique valuesUnique
보험자부담금 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:43:55.795803
Analysis finished2023-12-12 22:43:58.630727
Duration2.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

진료년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2022
725 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 725
100.0%

Length

2023-12-13T07:43:58.693780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:43:58.790985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 725
100.0%

시도
Categorical

Distinct17
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
경기
 
48
서울
 
47
충북
 
47
대구
 
46
경남
 
46
Other values (12)
491 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기 48
 
6.6%
서울 47
 
6.5%
충북 47
 
6.5%
대구 46
 
6.3%
경남 46
 
6.3%
부산 45
 
6.2%
전남 45
 
6.2%
전북 44
 
6.1%
충남 44
 
6.1%
광주 44
 
6.1%
Other values (7) 269
37.1%

Length

2023-12-13T07:43:58.898046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 48
 
6.6%
서울 47
 
6.5%
충북 47
 
6.5%
대구 46
 
6.3%
경남 46
 
6.3%
부산 45
 
6.2%
전남 45
 
6.2%
광주 44
 
6.1%
충남 44
 
6.1%
전북 44
 
6.1%
Other values (7) 269
37.1%

진료과목
Text

Distinct51
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2023-12-13T07:43:59.110684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length4.8868966
Min length2

Characters and Unicode

Total characters3543
Distinct characters79
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row일반의
2nd row내과
3rd row신경과
4th row정신건강의학과
5th row외과
ValueCountFrequency (%)
구강악안면외과 17
 
2.3%
방사선종양학과 17
 
2.3%
비뇨의학과 17
 
2.3%
영상의학과 17
 
2.3%
이비인후과 17
 
2.3%
핵의학과 17
 
2.3%
가정의학과 17
 
2.3%
응급의학과 17
 
2.3%
소아치과 17
 
2.3%
치과보철과 17
 
2.3%
Other values (41) 555
76.6%
2023-12-13T07:43:59.432626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 751
21.2%
학 230
 
6.5%
의 227
 
6.4%
방 136
 
3.8%
치 127
 
3.6%
외 102
 
2.9%
한 94
 
2.7%
소 81
 
2.3%
정 80
 
2.3%
부 79
 
2.2%
Other values (69) 1636
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3511
99.1%
Other Punctuation 30
 
0.8%
Decimal Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 751
21.4%
학 230
 
6.6%
의 227
 
6.5%
방 136
 
3.9%
치 127
 
3.6%
외 102
 
2.9%
한 94
 
2.7%
소 81
 
2.3%
정 80
 
2.3%
부 79
 
2.3%
Other values (66) 1604
45.7%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
3 1
50.0%
Other Punctuation
ValueCountFrequency (%)
· 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3511
99.1%
Common 32
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 751
21.4%
학 230
 
6.6%
의 227
 
6.5%
방 136
 
3.9%
치 127
 
3.6%
외 102
 
2.9%
한 94
 
2.7%
소 81
 
2.3%
정 80
 
2.3%
부 79
 
2.3%
Other values (66) 1604
45.7%
Common
ValueCountFrequency (%)
· 30
93.8%
2 1
 
3.1%
3 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3511
99.1%
None 30
 
0.8%
ASCII 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 751
21.4%
학 230
 
6.6%
의 227
 
6.5%
방 136
 
3.9%
치 127
 
3.6%
외 102
 
2.9%
한 94
 
2.7%
소 81
 
2.3%
정 80
 
2.3%
부 79
 
2.3%
Other values (66) 1604
45.7%
None
ValueCountFrequency (%)
· 30
100.0%
ASCII
ValueCountFrequency (%)
2 1
50.0%
3 1
50.0%

환자수
Real number (ℝ)

HIGH CORRELATION 

Distinct636
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25857.717
Minimum1
Maximum1694777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-13T07:43:59.585588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.2
Q1251
median2765
Q317830
95-th percentile125697.6
Maximum1694777
Range1694776
Interquartile range (IQR)17579

Descriptive statistics

Standard deviation94493.729
Coefficient of variation (CV)3.6543724
Kurtosis177.89052
Mean25857.717
Median Absolute Deviation (MAD)2724
Skewness11.626492
Sum18746845
Variance8.9290648 × 109
MonotonicityNot monotonic
2023-12-13T07:43:59.739411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
1.4%
2 8
 
1.1%
4 5
 
0.7%
6 5
 
0.7%
3 4
 
0.6%
60 4
 
0.6%
16 4
 
0.6%
70 3
 
0.4%
20 3
 
0.4%
42 3
 
0.4%
Other values (626) 676
93.2%
ValueCountFrequency (%)
1 10
1.4%
2 8
1.1%
3 4
 
0.6%
4 5
0.7%
5 3
 
0.4%
6 5
0.7%
7 1
 
0.1%
9 1
 
0.1%
10 2
 
0.3%
11 2
 
0.3%
ValueCountFrequency (%)
1694777 1
0.1%
1276542 1
0.1%
457042 1
0.1%
419917 1
0.1%
416475 1
0.1%
349436 1
0.1%
346406 1
0.1%
331041 1
0.1%
307765 1
0.1%
293230 1
0.1%

명세서청구건수
Real number (ℝ)

HIGH CORRELATION 

Distinct664
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50488.979
Minimum1
Maximum4154021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-13T07:43:59.888228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q1439
median4982
Q329964
95-th percentile239764.8
Maximum4154021
Range4154020
Interquartile range (IQR)29525

Descriptive statistics

Standard deviation212644.53
Coefficient of variation (CV)4.2117019
Kurtosis231.95872
Mean50488.979
Median Absolute Deviation (MAD)4925
Skewness13.564336
Sum36604510
Variance4.5217695 × 1010
MonotonicityNot monotonic
2023-12-13T07:44:00.049539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
1.4%
2 8
 
1.1%
4 6
 
0.8%
6 3
 
0.4%
20 3
 
0.4%
71 3
 
0.4%
42 3
 
0.4%
10 3
 
0.4%
114 3
 
0.4%
3 3
 
0.4%
Other values (654) 680
93.8%
ValueCountFrequency (%)
1 10
1.4%
2 8
1.1%
3 3
 
0.4%
4 6
0.8%
5 2
 
0.3%
6 3
 
0.4%
7 1
 
0.1%
8 2
 
0.3%
9 1
 
0.1%
10 3
 
0.4%
ValueCountFrequency (%)
4154021 1
0.1%
2821040 1
0.1%
900865 1
0.1%
889199 1
0.1%
816820 1
0.1%
754047 1
0.1%
731344 1
0.1%
673650 1
0.1%
642143 1
0.1%
546725 1
0.1%

입내원일수
Real number (ℝ)

HIGH CORRELATION 

Distinct679
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71127.634
Minimum0
Maximum5178893
Zeros4
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-13T07:44:00.241225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.2
Q1662
median5895
Q343724
95-th percentile346523.6
Maximum5178893
Range5178893
Interquartile range (IQR)43062

Descriptive statistics

Standard deviation273752.38
Coefficient of variation (CV)3.8487486
Kurtosis206.19057
Mean71127.634
Median Absolute Deviation (MAD)5849
Skewness12.613831
Sum51567535
Variance7.4940368 × 1010
MonotonicityNot monotonic
2023-12-13T07:44:00.363575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 7
 
1.0%
1 6
 
0.8%
4 5
 
0.7%
0 4
 
0.6%
10 3
 
0.4%
176 2
 
0.3%
3028 2
 
0.3%
1575 2
 
0.3%
2290 2
 
0.3%
439 2
 
0.3%
Other values (669) 690
95.2%
ValueCountFrequency (%)
0 4
0.6%
1 6
0.8%
2 7
1.0%
3 2
 
0.3%
4 5
0.7%
5 2
 
0.3%
6 2
 
0.3%
7 1
 
0.1%
8 1
 
0.1%
10 3
0.4%
ValueCountFrequency (%)
5178893 1
0.1%
3593384 1
0.1%
1248143 1
0.1%
1229049 1
0.1%
1220807 1
0.1%
1015677 1
0.1%
995813 1
0.1%
839020 1
0.1%
826344 1
0.1%
798401 1
0.1%

요양급여비용총액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3211961 × 1010
Minimum0
Maximum1.4155512 × 1012
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-13T07:44:00.487232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile787936
Q161812820
median6.8102871 × 108
Q36.3670476 × 109
95-th percentile5.6146824 × 1010
Maximum1.4155512 × 1012
Range1.4155512 × 1012
Interquartile range (IQR)6.3052347 × 109

Descriptive statistics

Standard deviation6.5652665 × 1010
Coefficient of variation (CV)4.9691839
Kurtosis306.09238
Mean1.3211961 × 1010
Median Absolute Deviation (MAD)6.7638088 × 108
Skewness15.657088
Sum9.5786719 × 1012
Variance4.3102724 × 1021
MonotonicityNot monotonic
2023-12-13T07:44:00.656591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800980250 1
 
0.1%
13020330 1
 
0.1%
458027860 1
 
0.1%
156655150 1
 
0.1%
6079430 1
 
0.1%
38089620 1
 
0.1%
104989291120 1
 
0.1%
10905383480 1
 
0.1%
6041659990 1
 
0.1%
23139127070 1
 
0.1%
Other values (715) 715
98.6%
ValueCountFrequency (%)
0 1
0.1%
2300 1
0.1%
16260 1
0.1%
21710 1
0.1%
24120 1
0.1%
28240 1
0.1%
39780 1
0.1%
59560 1
0.1%
59840 1
0.1%
70570 1
0.1%
ValueCountFrequency (%)
1415551224940 1
0.1%
707119054600 1
0.1%
335919641930 1
0.1%
239329190600 1
0.1%
235385494760 1
0.1%
233101831550 1
0.1%
208176155840 1
0.1%
185809382660 1
0.1%
183434012750 1
0.1%
180433993880 1
0.1%

보험자부담금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0453114 × 1010
Minimum0
Maximum1.1707843 × 1012
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-13T07:44:00.808980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile492920
Q144508930
median4.878541 × 108
Q34.5883069 × 109
95-th percentile4.3396453 × 1010
Maximum1.1707843 × 1012
Range1.1707843 × 1012
Interquartile range (IQR)4.543798 × 109

Descriptive statistics

Standard deviation5.3805405 × 1010
Coefficient of variation (CV)5.1473085
Kurtosis315.76037
Mean1.0453114 × 1010
Median Absolute Deviation (MAD)4.8518722 × 108
Skewness15.935681
Sum7.578508 × 1012
Variance2.8950216 × 1021
MonotonicityNot monotonic
2023-12-13T07:44:01.208249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
695018940 1
 
0.1%
8318680 1
 
0.1%
315638100 1
 
0.1%
101765330 1
 
0.1%
4230590 1
 
0.1%
28335810 1
 
0.1%
83302837560 1
 
0.1%
7570564340 1
 
0.1%
4428565490 1
 
0.1%
18977197720 1
 
0.1%
Other values (715) 715
98.6%
ValueCountFrequency (%)
0 1
0.1%
1400 1
0.1%
9760 1
0.1%
13110 1
0.1%
14240 1
0.1%
15720 1
0.1%
29860 1
0.1%
31830 1
0.1%
36040 1
0.1%
45970 1
0.1%
ValueCountFrequency (%)
1170784318750 1
0.1%
563896496910 1
0.1%
290429449090 1
0.1%
192694656870 1
0.1%
191980084300 1
0.1%
189274266330 1
0.1%
156314991720 1
0.1%
154335096210 1
0.1%
144724466430 1
0.1%
136651268440 1
0.1%

Interactions

2023-12-13T07:43:58.025686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.134207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.657215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.079130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.536344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:58.116564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.229897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.746985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.174310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.642569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:58.189297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.333744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.823164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.262576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.745430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:58.270375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.445269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.896827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.348655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.843157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:58.365765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.544288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:56.988756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.449980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:43:57.934304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:44:01.315135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도진료과목환자수명세서청구건수입내원일수요양급여비용총액보험자부담금
시도1.0000.0000.1940.1570.1330.0000.000
진료과목0.0001.0000.3940.0000.3670.2180.218
환자수0.1940.3941.0000.9880.9950.9800.980
명세서청구건수0.1570.0000.9881.0000.9930.9780.978
입내원일수0.1330.3670.9950.9931.0000.9800.980
요양급여비용총액0.0000.2180.9800.9780.9801.0001.000
보험자부담금0.0000.2180.9800.9780.9801.0001.000
2023-12-13T07:44:01.434463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자수명세서청구건수입내원일수요양급여비용총액보험자부담금시도
환자수1.0000.9900.9720.9440.9350.100
명세서청구건수0.9901.0000.9850.9630.9560.080
입내원일수0.9720.9851.0000.9810.9760.068
요양급여비용총액0.9440.9630.9811.0000.9990.000
보험자부담금0.9350.9560.9760.9991.0000.000
시도0.1000.0800.0680.0000.0001.000

Missing values

2023-12-13T07:43:58.481711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:43:58.587230image/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

진료년도시도진료과목환자수명세서청구건수입내원일수요양급여비용총액보험자부담금
02022서울일반의3793971670800980250695018940
12022서울내과16947774154021517889314155512249401170784318750
22022서울신경과34640654672569063212040962417083413398960
32022서울정신건강의학과1442362874613674904286509454028604695570
42022서울외과278225528446826344335919641930290429449090
52022서울정형외과3494366736501015677208176155840156314991720
62022서울신경외과189952314897531989185809382660154335096210
72022서울심장혈관흉부외과395726470913517610963414838099324187560
82022서울성형외과2966868878970641953882218015545387800
92022서울마취통증의학과32307508575610851652911503032744720
진료년도시도진료과목환자수명세서청구건수입내원일수요양급여비용총액보험자부담금
7152022세종구강악안면외과2473323292162149013224490
7162022세종치과보철과1091351291814307012681540
7172022세종치과교정과121212400640240840
7182022세종소아치과3714494491927584011811940
7192022세종치주과2823103081966367011613770
7202022세종치과보존과5448228205264544030913290
7212022세종한방내과2142561351627438012168020
7222022세종한방안·이비인후·피부과101010440400262300
7232022세종침구과527961160912323515092574350
7242022세종한방재활의학과741112682959153022189910