Overview

Dataset statistics

Number of variables7
Number of observations55
Missing cells36
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory64.4 B

Variable types

Text1
Numeric6

Dataset

Description근로복지공단 지사별 의료기관별 환자진료수 현황정보입니다.(2012년4분기)
Author근로복지공단
URLhttps://www.data.go.kr/data/15051516/fileData.do

Alerts

상급종합 has 25 (45.5%) missing valuesMissing
종합병원 has 2 (3.6%) missing valuesMissing
치과 has 2 (3.6%) missing valuesMissing
한방 has 7 (12.7%) missing valuesMissing
지사명 has unique valuesUnique
병원 has unique valuesUnique
의원 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:51:09.583993
Analysis finished2023-12-12 04:51:14.144768
Duration4.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지사명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-12T13:51:14.318318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.7454545
Min length4

Characters and Unicode

Total characters261
Distinct characters56
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

Unique55 ?
Unique (%)100.0%

Sample

1st row서울지역본부
2nd row서울강남지사
3rd row서울서초지사
4th row서울동부지사
5th row서울성동지사
ValueCountFrequency (%)
서울지역본부 1
 
1.8%
포항지사 1
 
1.8%
영주지사 1
 
1.8%
안동지사 1
 
1.8%
경인지역본부 1
 
1.8%
인천북부지사 1
 
1.8%
수원지사 1
 
1.8%
부천지사 1
 
1.8%
안양지사 1
 
1.8%
안산지사 1
 
1.8%
Other values (45) 45
81.8%
2023-12-12T13:51:14.740319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 55
21.1%
사 49
18.8%
부 22
 
8.4%
서 12
 
4.6%
산 11
 
4.2%
울 10
 
3.8%
주 8
 
3.1%
역 6
 
2.3%
본 6
 
2.3%
북 4
 
1.5%
Other values (46) 78
29.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 261
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 55
21.1%
사 49
18.8%
부 22
 
8.4%
서 12
 
4.6%
산 11
 
4.2%
울 10
 
3.8%
주 8
 
3.1%
역 6
 
2.3%
본 6
 
2.3%
북 4
 
1.5%
Other values (46) 78
29.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 261
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 55
21.1%
사 49
18.8%
부 22
 
8.4%
서 12
 
4.6%
산 11
 
4.2%
울 10
 
3.8%
주 8
 
3.1%
역 6
 
2.3%
본 6
 
2.3%
북 4
 
1.5%
Other values (46) 78
29.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 261
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 55
21.1%
사 49
18.8%
부 22
 
8.4%
서 12
 
4.6%
산 11
 
4.2%
울 10
 
3.8%
주 8
 
3.1%
역 6
 
2.3%
본 6
 
2.3%
북 4
 
1.5%
Other values (46) 78
29.9%

상급종합
Real number (ℝ)

MISSING 

Distinct30
Distinct (%)100.0%
Missing25
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean3413791.2
Minimum1613495
Maximum8363549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T13:51:14.873804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1613495
5-th percentile2080624
Q12679140.2
median2966998.5
Q33571847
95-th percentile5622577.8
Maximum8363549
Range6750054
Interquartile range (IQR)892706.75

Descriptive statistics

Standard deviation1359705.3
Coefficient of variation (CV)0.39829773
Kurtosis5.2781063
Mean3413791.2
Median Absolute Deviation (MAD)512254
Skewness2.0099589
Sum1.0241374 × 108
Variance1.8487984 × 1012
MonotonicityNot monotonic
2023-12-12T13:51:15.006210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2944963 1
 
1.8%
2893969 1
 
1.8%
3418217 1
 
1.8%
2989034 1
 
1.8%
4970120 1
 
1.8%
2361056 1
 
1.8%
3013356 1
 
1.8%
2873920 1
 
1.8%
2666649 1
 
1.8%
6156407 1
 
1.8%
Other values (20) 20
36.4%
(Missing) 25
45.5%
ValueCountFrequency (%)
1613495 1
1.8%
1907176 1
1.8%
2292616 1
1.8%
2361056 1
1.8%
2371102 1
1.8%
2518592 1
1.8%
2546876 1
1.8%
2666649 1
1.8%
2716614 1
1.8%
2864756 1
1.8%
ValueCountFrequency (%)
8363549 1
1.8%
6156407 1
1.8%
4970120 1
1.8%
4862583 1
1.8%
4819773 1
1.8%
4462377 1
1.8%
3979778 1
1.8%
3580403 1
1.8%
3546179 1
1.8%
3540288 1
1.8%

종합병원
Real number (ℝ)

MISSING 

Distinct53
Distinct (%)100.0%
Missing2
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean3974627.4
Minimum1909878
Maximum9850358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T13:51:15.153068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1909878
5-th percentile2314674.4
Q13021099
median3237106
Q34438737
95-th percentile7737630.2
Maximum9850358
Range7940480
Interquartile range (IQR)1417638

Descriptive statistics

Standard deviation1714260.6
Coefficient of variation (CV)0.43130096
Kurtosis3.0511279
Mean3974627.4
Median Absolute Deviation (MAD)489147
Skewness1.8047196
Sum2.1065525 × 108
Variance2.9386895 × 1012
MonotonicityNot monotonic
2023-12-12T13:51:15.328599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3103281 1
 
1.8%
3096046 1
 
1.8%
3149261 1
 
1.8%
5023758 1
 
1.8%
2742977 1
 
1.8%
3492495 1
 
1.8%
3465588 1
 
1.8%
2787642 1
 
1.8%
3091902 1
 
1.8%
2699388 1
 
1.8%
Other values (43) 43
78.2%
(Missing) 2
 
3.6%
ValueCountFrequency (%)
1909878 1
1.8%
2215684 1
1.8%
2271274 1
1.8%
2343608 1
1.8%
2383424 1
1.8%
2699388 1
1.8%
2742977 1
1.8%
2787642 1
1.8%
2949907 1
1.8%
2986649 1
1.8%
ValueCountFrequency (%)
9850358 1
1.8%
8797048 1
1.8%
8584223 1
1.8%
7173235 1
1.8%
6930886 1
1.8%
6243022 1
1.8%
6059357 1
1.8%
5403900 1
1.8%
5394380 1
1.8%
5211503 1
1.8%

병원
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3267809.4
Minimum1038656
Maximum10767273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T13:51:15.507229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1038656
5-th percentile1874715.6
Q12370486
median2877885
Q33961569
95-th percentile5435771.2
Maximum10767273
Range9728617
Interquartile range (IQR)1591083

Descriptive statistics

Standard deviation1529105.2
Coefficient of variation (CV)0.46792975
Kurtosis9.8911221
Mean3267809.4
Median Absolute Deviation (MAD)698346
Skewness2.4781042
Sum1.7972952 × 108
Variance2.3381628 × 1012
MonotonicityNot monotonic
2023-12-12T13:51:15.699885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3012513 1
 
1.8%
1167655 1
 
1.8%
5225404 1
 
1.8%
10767273 1
 
1.8%
2342999 1
 
1.8%
2397973 1
 
1.8%
3040930 1
 
1.8%
4482550 1
 
1.8%
3580517 1
 
1.8%
2816629 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
1038656 1
1.8%
1167655 1
1.8%
1818864 1
1.8%
1898652 1
1.8%
1992666 1
1.8%
2068410 1
1.8%
2102498 1
1.8%
2102523 1
1.8%
2128914 1
1.8%
2131844 1
1.8%
ValueCountFrequency (%)
10767273 1
1.8%
6614673 1
1.8%
5926628 1
1.8%
5225404 1
1.8%
4910370 1
1.8%
4796814 1
1.8%
4614185 1
1.8%
4532817 1
1.8%
4482550 1
1.8%
4469541 1
1.8%

의원
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1254576
Minimum417068
Maximum2263372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T13:51:15.874219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum417068
5-th percentile723873.9
Q11099762
median1243697
Q31366176
95-th percentile1655173.7
Maximum2263372
Range1846304
Interquartile range (IQR)266414

Descriptive statistics

Standard deviation310882.46
Coefficient of variation (CV)0.24779883
Kurtosis3.593996
Mean1254576
Median Absolute Deviation (MAD)131221
Skewness0.62188663
Sum69001680
Variance9.6647904 × 1010
MonotonicityNot monotonic
2023-12-12T13:51:16.035215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1203998 1
 
1.8%
678838 1
 
1.8%
1087841 1
 
1.8%
743175 1
 
1.8%
1564864 1
 
1.8%
1057121 1
 
1.8%
1150044 1
 
1.8%
1204020 1
 
1.8%
1433822 1
 
1.8%
1040281 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
417068 1
1.8%
559881 1
1.8%
678838 1
1.8%
743175 1
1.8%
990687 1
1.8%
991003 1
1.8%
1039486 1
1.8%
1040281 1
1.8%
1040935 1
1.8%
1057121 1
1.8%
ValueCountFrequency (%)
2263372 1
1.8%
2238281 1
1.8%
1842626 1
1.8%
1574837 1
1.8%
1565373 1
1.8%
1564864 1
1.8%
1497214 1
1.8%
1436909 1
1.8%
1435856 1
1.8%
1433822 1
1.8%

치과
Real number (ℝ)

MISSING 

Distinct53
Distinct (%)100.0%
Missing2
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean1584536.6
Minimum343180
Maximum3716000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T13:51:16.178389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum343180
5-th percentile745087.6
Q11115755
median1597090
Q31946107
95-th percentile2527342.2
Maximum3716000
Range3372820
Interquartile range (IQR)830352

Descriptive statistics

Standard deviation622319.83
Coefficient of variation (CV)0.39274562
Kurtosis1.2905353
Mean1584536.6
Median Absolute Deviation (MAD)479622
Skewness0.68174442
Sum83980442
Variance3.8728197 × 1011
MonotonicityNot monotonic
2023-12-12T13:51:16.324038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010763 1
 
1.8%
744490 1
 
1.8%
2105136 1
 
1.8%
1358955 1
 
1.8%
1824857 1
 
1.8%
1597090 1
 
1.8%
955723 1
 
1.8%
1246023 1
 
1.8%
1154362 1
 
1.8%
1383590 1
 
1.8%
Other values (43) 43
78.2%
(Missing) 2
 
3.6%
ValueCountFrequency (%)
343180 1
1.8%
688640 1
1.8%
744490 1
1.8%
745486 1
1.8%
767544 1
1.8%
824313 1
1.8%
891560 1
1.8%
955723 1
1.8%
962844 1
1.8%
981665 1
1.8%
ValueCountFrequency (%)
3716000 1
1.8%
2692159 1
1.8%
2555223 1
1.8%
2508755 1
1.8%
2324922 1
1.8%
2321740 1
1.8%
2246395 1
1.8%
2192940 1
1.8%
2184260 1
1.8%
2133189 1
1.8%

한방
Real number (ℝ)

MISSING 

Distinct48
Distinct (%)100.0%
Missing7
Missing (%)12.7%
Infinite0
Infinite (%)0.0%
Mean2322343.1
Minimum212443
Maximum11796367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T13:51:16.478447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum212443
5-th percentile302375
Q1663946
median1264678.5
Q32636037.2
95-th percentile7639173.4
Maximum11796367
Range11583924
Interquartile range (IQR)1972091.2

Descriptive statistics

Standard deviation2568992.6
Coefficient of variation (CV)1.1062072
Kurtosis3.8321416
Mean2322343.1
Median Absolute Deviation (MAD)748162
Skewness1.9879581
Sum1.1147247 × 108
Variance6.5997231 × 1012
MonotonicityNot monotonic
2023-12-12T13:51:16.633499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1187865 1
 
1.8%
2205896 1
 
1.8%
541510 1
 
1.8%
310790 1
 
1.8%
739223 1
 
1.8%
1184049 1
 
1.8%
7164923 1
 
1.8%
509773 1
 
1.8%
3465467 1
 
1.8%
1928455 1
 
1.8%
Other values (38) 38
69.1%
(Missing) 7
 
12.7%
ValueCountFrequency (%)
212443 1
1.8%
296860 1
1.8%
299260 1
1.8%
308160 1
1.8%
310790 1
1.8%
486787 1
1.8%
509773 1
1.8%
523260 1
1.8%
533322 1
1.8%
541510 1
1.8%
ValueCountFrequency (%)
11796367 1
1.8%
9244179 1
1.8%
7894539 1
1.8%
7164923 1
1.8%
6658483 1
1.8%
5670094 1
1.8%
5379947 1
1.8%
4578799 1
1.8%
4012483 1
1.8%
3465467 1
1.8%

Interactions

2023-12-12T13:51:13.194148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:09.826641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.411434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.015249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.573834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:12.177639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:13.280455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:09.939119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.496526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.117035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.677999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:12.292107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:13.375230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.049728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.613925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.218820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.793758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:12.403280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:13.482917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.140136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.728897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.316288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.893977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:12.512666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:13.604001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.233618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.829043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.407101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.988937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:12.627677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:13.744551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.324435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:10.915114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:11.491782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:12.082326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:12.733935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:51:16.762708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지사명상급종합종합병원병원의원치과한방
지사명1.0001.0001.0001.0001.0001.0001.000
상급종합1.0001.0000.0000.0000.5810.0000.000
종합병원1.0000.0001.0000.5320.0000.2410.102
병원1.0000.0000.5321.0000.4600.0000.000
의원1.0000.5810.0000.4601.0000.0000.000
치과1.0000.0000.2410.0000.0001.0000.000
한방1.0000.0000.1020.0000.0000.0001.000
2023-12-12T13:51:16.896363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상급종합종합병원병원의원치과한방
상급종합1.000-0.243-0.107-0.1500.3380.077
종합병원-0.2431.0000.0800.0400.0850.141
병원-0.1070.0801.0000.1480.0560.007
의원-0.1500.0400.1481.0000.0370.051
치과0.3380.0850.0560.0371.000-0.172
한방0.0770.1410.0070.051-0.1721.000

Missing values

2023-12-12T13:51:13.872653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:51:13.980072image/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.
2023-12-12T13:51:14.080031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

지사명상급종합종합병원병원의원치과한방
0서울지역본부286475630141793012513120399868864011796367
1서울강남지사32166416930886116765567883821929402338673
2서울서초지사3061154<NA>103865615748371093200212443
3서울동부지사23711023726253403249612269561727064974558
4서울성동지사3546179<NA>210249813671798915609244179
5서울서부지사446237733420062278894108867820767124578799
6서울남부지사254687660593572877885156537316961111858577
7서울북부지사19071764117032300882810988031831966867721
8서울관악지사354028831649734614185135431219461071302190
9의정부지사<NA>3005142311176811860682246395784667
지사명상급종합종합병원병원의원치과한방
45군산지사<NA>3982842288242413749183716000<NA>
46목포지사<NA>35668021898652112769815902002580290
47여수지사497012030009682714185121442021842601355505
48제주지사<NA>307617518188645598812123667821048
49대전지역본부298903453943804234251134384813175301924660
50유성지사<NA>9850358661467312436971438050523260
51청주지사3418217385190628261419906871712455669118
52충주지사<NA>71732354796814135565414823604012483
53천안지사2893969221568424243321040935981665791889
54보령지사<NA>29866495926628103948617922401759674