Overview

Dataset statistics

Number of variables8
Number of observations250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.2 KiB
Average record size in memory70.5 B

Variable types

Categorical2
Text1
Numeric5

Dataset

Description전체 의료기관의 시군구별 진료비 통계 / 진료일자 기준(심사분은 각 진료년+4개월) (예) 진료년월: 2020.1월~12월, 심사년월: 2020.1월~2021.4월 / 보험자: 건강보험
URLhttps://www.data.go.kr/data/15055561/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
환자수 has unique valuesUnique
명세서청구건수 has unique valuesUnique
입내원일수 has unique valuesUnique
요양급여비용총액 has unique valuesUnique
보험자부담금 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:26:17.804099
Analysis finished2023-12-12 20:26:21.177722
Duration3.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

진료년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2022
250 

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 250
100.0%

Length

2023-12-13T05:26:21.258169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:21.364543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 250
100.0%

시도
Categorical

Distinct17
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
경기
42 
서울
25 
경북
24 
경남
22 
전남
22 
Other values (12)
115 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기 42
16.8%
서울 25
10.0%
경북 24
9.6%
경남 22
8.8%
전남 22
8.8%
강원 18
7.2%
부산 16
 
6.4%
충남 16
 
6.4%
전북 15
 
6.0%
충북 14
 
5.6%
Other values (7) 36
14.4%

Length

2023-12-13T05:26:21.484093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 42
16.8%
서울 25
10.0%
경북 24
9.6%
경남 22
8.8%
전남 22
8.8%
강원 18
7.2%
부산 16
 
6.4%
충남 16
 
6.4%
전북 15
 
6.0%
충북 14
 
5.6%
Other values (7) 36
14.4%

시군구
Text

Distinct249
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-13T05:26:21.832861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.596
Min length2

Characters and Unicode

Total characters899
Distinct characters142
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique248 ?
Unique (%)99.2%

Sample

1st row강남구
2nd row강동구
3rd row강서구
4th row관악구
5th row구로구
ValueCountFrequency (%)
고성군 2
 
0.8%
임실군 1
 
0.4%
무안군 1
 
0.4%
진안군 1
 
0.4%
보령시 1
 
0.4%
아산시 1
 
0.4%
서산시 1
 
0.4%
논산시 1
 
0.4%
계룡시 1
 
0.4%
당진시 1
 
0.4%
Other values (239) 239
95.6%
2023-12-13T05:26:22.288121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 114
 
12.7%
군 85
 
9.5%
시 68
 
7.6%
산 43
 
4.8%
천 33
 
3.7%
주 29
 
3.2%
양 22
 
2.4%
성 21
 
2.3%
부 21
 
2.3%
동 20
 
2.2%
Other values (132) 443
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 899
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 114
 
12.7%
군 85
 
9.5%
시 68
 
7.6%
산 43
 
4.8%
천 33
 
3.7%
주 29
 
3.2%
양 22
 
2.4%
성 21
 
2.3%
부 21
 
2.3%
동 20
 
2.2%
Other values (132) 443
49.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 899
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 114
 
12.7%
군 85
 
9.5%
시 68
 
7.6%
산 43
 
4.8%
천 33
 
3.7%
주 29
 
3.2%
양 22
 
2.4%
성 21
 
2.3%
부 21
 
2.3%
동 20
 
2.2%
Other values (132) 443
49.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 899
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 114
 
12.7%
군 85
 
9.5%
시 68
 
7.6%
산 43
 
4.8%
천 33
 
3.7%
주 29
 
3.2%
양 22
 
2.4%
성 21
 
2.3%
부 21
 
2.3%
동 20
 
2.2%
Other values (132) 443
49.3%

환자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416097.75
Minimum6770
Maximum3241314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T05:26:22.438877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6770
5-th percentile30243
Q171372
median339197
Q3643885.75
95-th percentile1084158.6
Maximum3241314
Range3234544
Interquartile range (IQR)572513.75

Descriptive statistics

Standard deviation413955.35
Coefficient of variation (CV)0.9948512
Kurtosis9.0559559
Mean416097.75
Median Absolute Deviation (MAD)272864.5
Skewness2.1312035
Sum1.0402444 × 108
Variance1.7135903 × 1011
MonotonicityNot monotonic
2023-12-13T05:26:22.599984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3241314 1
 
0.4%
349707 1
 
0.4%
542328 1
 
0.4%
246323 1
 
0.4%
177615 1
 
0.4%
57357 1
 
0.4%
225726 1
 
0.4%
60222 1
 
0.4%
28786 1
 
0.4%
60647 1
 
0.4%
Other values (240) 240
96.0%
ValueCountFrequency (%)
6770 1
0.4%
13260 1
0.4%
20309 1
0.4%
23433 1
0.4%
24443 1
0.4%
25433 1
0.4%
25569 1
0.4%
27544 1
0.4%
27869 1
0.4%
28786 1
0.4%
ValueCountFrequency (%)
3241314 1
0.4%
2003434 1
0.4%
1912146 1
0.4%
1697807 1
0.4%
1429099 1
0.4%
1416166 1
0.4%
1382745 1
0.4%
1316564 1
0.4%
1227049 1
0.4%
1179460 1
0.4%

명세서청구건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3821793.1
Minimum36157
Maximum18364789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T05:26:22.748708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36157
5-th percentile303679.25
Q1950221
median3277797
Q35755985
95-th percentile9766531.5
Maximum18364789
Range18328632
Interquartile range (IQR)4805764

Descriptive statistics

Standard deviation3316648.6
Coefficient of variation (CV)0.86782527
Kurtosis1.6674279
Mean3821793.1
Median Absolute Deviation (MAD)2401896
Skewness1.1767648
Sum9.5544828 × 108
Variance1.1000158 × 1013
MonotonicityNot monotonic
2023-12-13T05:26:22.893694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18364789 1
 
0.4%
5062850 1
 
0.4%
5118182 1
 
0.4%
2842599 1
 
0.4%
2444935 1
 
0.4%
486875 1
 
0.4%
2624339 1
 
0.4%
969195 1
 
0.4%
346537 1
 
0.4%
1053446 1
 
0.4%
Other values (240) 240
96.0%
ValueCountFrequency (%)
36157 1
0.4%
100462 1
0.4%
102860 1
0.4%
208219 1
0.4%
217479 1
0.4%
224621 1
0.4%
233807 1
0.4%
259180 1
0.4%
260131 1
0.4%
265657 1
0.4%
ValueCountFrequency (%)
18364789 1
0.4%
15521449 1
0.4%
15370666 1
0.4%
13640819 1
0.4%
13413992 1
0.4%
11413392 1
0.4%
11015529 1
0.4%
10930673 1
0.4%
10623072 1
0.4%
10594896 1
0.4%

입내원일수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4297233.3
Minimum39569
Maximum20040702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T05:26:23.036367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39569
5-th percentile308036.9
Q11030600.8
median3695739
Q36633393.5
95-th percentile10702079
Maximum20040702
Range20001133
Interquartile range (IQR)5602792.8

Descriptive statistics

Standard deviation3682391.6
Coefficient of variation (CV)0.85692149
Kurtosis1.5199323
Mean4297233.3
Median Absolute Deviation (MAD)2717407.5
Skewness1.1294896
Sum1.0743083 × 109
Variance1.3560008 × 1013
MonotonicityNot monotonic
2023-12-13T05:26:23.210858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20040702 1
 
0.4%
5866206 1
 
0.4%
5604909 1
 
0.4%
3024040 1
 
0.4%
2938641 1
 
0.4%
516506 1
 
0.4%
2814805 1
 
0.4%
1156713 1
 
0.4%
347101 1
 
0.4%
1232876 1
 
0.4%
Other values (240) 240
96.0%
ValueCountFrequency (%)
39569 1
0.4%
100551 1
0.4%
105549 1
0.4%
208200 1
0.4%
217202 1
0.4%
231852 1
0.4%
233579 1
0.4%
265276 1
0.4%
294394 1
0.4%
296675 1
0.4%
ValueCountFrequency (%)
20040702 1
0.4%
17622060 1
0.4%
17392740 1
0.4%
14840197 1
0.4%
14379066 1
0.4%
13024695 1
0.4%
12066720 1
0.4%
11766626 1
0.4%
11639317 1
0.4%
11445474 1
0.4%

요양급여비용총액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3121162 × 1011
Minimum1.6934903 × 109
Maximum2.76597 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T05:26:23.403699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6934903 × 109
5-th percentile1.2034269 × 1010
Q14.9940427 × 1010
median2.30441 × 1011
Q34.9165825 × 1011
95-th percentile9.6725485 × 1011
Maximum2.76597 × 1012
Range2.7642765 × 1012
Interquartile range (IQR)4.4171782 × 1011

Descriptive statistics

Standard deviation3.8131311 × 1011
Coefficient of variation (CV)1.1512673
Kurtosis9.8376783
Mean3.3121162 × 1011
Median Absolute Deviation (MAD)1.9385706 × 1011
Skewness2.4834786
Sum8.2802906 × 1013
Variance1.4539969 × 1023
MonotonicityNot monotonic
2023-12-13T05:26:23.867318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2765970000000 1
 
0.4%
344569000000 1
 
0.4%
322785000000 1
 
0.4%
158642000000 1
 
0.4%
168908000000 1
 
0.4%
16813323530 1
 
0.4%
144483000000 1
 
0.4%
64636174980 1
 
0.4%
10562998730 1
 
0.4%
57766338420 1
 
0.4%
Other values (240) 240
96.0%
ValueCountFrequency (%)
1693490310 1
0.4%
3246505930 1
0.4%
3395499980 1
0.4%
5398276920 1
0.4%
6338403890 1
0.4%
6734911610 1
0.4%
7674430920 1
0.4%
8082060250 1
0.4%
9089290650 1
0.4%
9145790710 1
0.4%
ValueCountFrequency (%)
2765970000000 1
0.4%
2328110000000 1
0.4%
1757240000000 1
0.4%
1568940000000 1
0.4%
1519330000000 1
0.4%
1463610000000 1
0.4%
1452530000000 1
0.4%
1154430000000 1
0.4%
1008430000000 1
0.4%
1007130000000 1
0.4%

보험자부담금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5414378 × 1011
Minimum1.25002 × 109
Maximum2.19843 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T05:26:24.023598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.25002 × 109
5-th percentile8.9947489 × 109
Q13.8156499 × 1010
median1.697765 × 1011
Q33.7098425 × 1011
95-th percentile7.3757155 × 1011
Maximum2.19843 × 1012
Range2.19718 × 1012
Interquartile range (IQR)3.3282775 × 1011

Descriptive statistics

Standard deviation2.9947864 × 1011
Coefficient of variation (CV)1.1783827
Kurtosis10.861957
Mean2.5414378 × 1011
Median Absolute Deviation (MAD)1.4447937 × 1011
Skewness2.6252947
Sum6.3535944 × 1013
Variance8.9687456 × 1022
MonotonicityNot monotonic
2023-12-13T05:26:24.188651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2198430000000 1
 
0.4%
260688000000 1
 
0.4%
244066000000 1
 
0.4%
117152000000 1
 
0.4%
126865000000 1
 
0.4%
12608227180 1
 
0.4%
107580000000 1
 
0.4%
49394051690 1
 
0.4%
8156975070 1
 
0.4%
43672934410 1
 
0.4%
Other values (240) 240
96.0%
ValueCountFrequency (%)
1250019970 1
0.4%
2378268040 1
0.4%
2487967670 1
0.4%
4119787400 1
0.4%
4818085720 1
0.4%
5156662810 1
0.4%
5816568910 1
0.4%
6393562200 1
0.4%
6719589850 1
0.4%
6928936070 1
0.4%
ValueCountFrequency (%)
2198430000000 1
0.4%
1878990000000 1
0.4%
1371240000000 1
0.4%
1245220000000 1
0.4%
1241380000000 1
0.4%
1161440000000 1
0.4%
1110200000000 1
0.4%
881336000000 1
0.4%
814036000000 1
0.4%
794119000000 1
0.4%

Interactions

2023-12-13T05:26:20.416075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.178646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.752289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.338080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.886618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:20.520778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.297422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.870427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.448659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:20.002886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:20.630011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.422575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.985396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.574136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:20.099064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:20.730859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.529325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.120398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.697997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:20.212255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:20.835924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.648407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.236916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.793363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:20.312328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:26:24.280638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도환자수명세서청구건수입내원일수요양급여비용총액보험자부담금
시도1.0000.4130.4910.4850.3450.340
환자수0.4131.0000.8930.8980.9290.922
명세서청구건수0.4910.8931.0000.9960.9120.908
입내원일수0.4850.8980.9961.0000.8960.892
요양급여비용총액0.3450.9290.9120.8961.0001.000
보험자부담금0.3400.9220.9080.8921.0001.000
2023-12-13T05:26:24.427029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자수명세서청구건수입내원일수요양급여비용총액보험자부담금시도
환자수1.0000.9780.9760.9620.9600.184
명세서청구건수0.9781.0000.9980.9640.9620.212
입내원일수0.9760.9981.0000.9730.9710.209
요양급여비용총액0.9620.9640.9731.0001.0000.143
보험자부담금0.9600.9620.9711.0001.0000.141
시도0.1840.2120.2090.1430.1411.000

Missing values

2023-12-13T05:26:20.962635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:26:21.125067image/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

진료년도시도시군구환자수명세서청구건수입내원일수요양급여비용총액보험자부담금
02022서울강남구3241314183647892004070227659700000002198430000000
12022서울강동구107403710623072117666261008430000000768855000000
22022서울강서구11752061059489611392243848979000000641184000000
32022서울관악구82167373644867700065442447000000330719000000
42022서울구로구91326081358359075055813000000000632068000000
52022서울도봉구47815546924435096450289516000000217852000000
62022서울동대문구90929775232148424238721705000000544082000000
72022서울동작구91779870330917485229660026000000506827000000
82022서울마포구94845968585137006861339876000000253569000000
92022서울서대문구11076317941564890966615193300000001241380000000
진료년도시도시군구환자수명세서청구건수입내원일수요양급여비용총액보험자부담금
2402022경남창원진해구26758127740713128709152911000000114056000000
2412022경남창원의창구65470050827225990479506402000000374213000000
2422022경남창원성산구53117642911584883432363200000000277802000000
2432022경남통영시16550723716362660440140862000000105841000000
2442022경남밀양시1240131640117190471810940600000083056895000
2452022경남거제시28431137064154100027231779000000169019000000
2462022경남양산시60658565616057875279735054000000579324000000
2472022제주서귀포시28659532050083281432146604000000110334000000
2482022제주제주시703217958138410458014700398000000538314000000
2492022세종세종시53884460699786307221363207000000276135000000