Overview

Dataset statistics

Number of variables11
Number of observations120
Missing cells25
Missing cells (%)1.9%
Duplicate rows1
Duplicate rows (%)0.8%
Total size in memory11.5 KiB
Average record size in memory98.1 B

Variable types

DateTime1
Numeric9
Text1

Dataset

Description병원 외래입원 수입금 현황 데이터로 (입원) 본인부담금, (입원)기관부담금(보험), (입원)기관부담금(급여), (외래)기관부담금(보험), (외래)기관부담금(급여), 토지 및 건물대여료, 면허료 및 수수료 등 데이트를 제공하고 있습니다.
Author질병관리청 국립마산병원
URLhttps://www.data.go.kr/data/3048700/fileData.do

Alerts

Dataset has 1 (0.8%) duplicate rowsDuplicates
년월 has 2 (1.7%) missing valuesMissing
(입원)본인부담금 has 2 (1.7%) missing valuesMissing
(입원)기관부담금(보험) has 2 (1.7%) missing valuesMissing
(입원)기관부담금(급여) has 2 (1.7%) missing valuesMissing
(외래)본인부담금 has 2 (1.7%) missing valuesMissing
(외래)기관부담금(보험) has 2 (1.7%) missing valuesMissing
(외래)기관부담금(급여) has 4 (3.3%) missing valuesMissing
토지 및 건물대여료 has 2 (1.7%) missing valuesMissing
면허료 및 수수료 has 2 (1.7%) missing valuesMissing
이월금(잉여금) 및 전입금 has 3 (2.5%) missing valuesMissing
기타수입 has 2 (1.7%) missing valuesMissing
(입원)본인부담금 has 3 (2.5%) zerosZeros
(입원)기관부담금(급여) has 7 (5.8%) zerosZeros
(외래)기관부담금(보험) has 8 (6.7%) zerosZeros
(외래)기관부담금(급여) has 10 (8.3%) zerosZeros
토지 및 건물대여료 has 2 (1.7%) zerosZeros
면허료 및 수수료 has 33 (27.5%) zerosZeros
이월금(잉여금) 및 전입금 has 91 (75.8%) zerosZeros
기타수입 has 11 (9.2%) zerosZeros

Reproduction

Analysis started2023-12-12 18:09:20.897009
Analysis finished2023-12-12 18:09:29.969704
Duration9.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월
Date

MISSING 

Distinct118
Distinct (%)100.0%
Missing2
Missing (%)1.7%
Memory size1.1 KiB
Minimum2014-01-01 00:00:00
Maximum2023-10-01 00:00:00
2023-12-13T03:09:30.030231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:30.453318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

(입원)본인부담금
Real number (ℝ)

MISSING  ZEROS 

Distinct116
Distinct (%)98.3%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean9744804.9
Minimum-77240
Maximum2.5518325 × 108
Zeros3
Zeros (%)2.5%
Negative1
Negative (%)0.8%
Memory size1.2 KiB
2023-12-13T03:09:30.624356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-77240
5-th percentile20225
Q1684367.5
median1311410
Q34713715
95-th percentile43308166
Maximum2.5518325 × 108
Range2.5526049 × 108
Interquartile range (IQR)4029347.5

Descriptive statistics

Standard deviation30065882
Coefficient of variation (CV)3.0853241
Kurtosis42.054678
Mean9744804.9
Median Absolute Deviation (MAD)1182620
Skewness5.9906574
Sum1.149887 × 109
Variance9.0395724 × 1014
MonotonicityNot monotonic
2023-12-13T03:09:30.763428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
2.5%
23658100 1
 
0.8%
4093860 1
 
0.8%
312480 1
 
0.8%
1986540 1
 
0.8%
3116730 1
 
0.8%
17689530 1
 
0.8%
31927930 1
 
0.8%
6726070 1
 
0.8%
5534580 1
 
0.8%
Other values (106) 106
88.3%
(Missing) 2
 
1.7%
ValueCountFrequency (%)
-77240 1
 
0.8%
0 3
2.5%
890 1
 
0.8%
18780 1
 
0.8%
20480 1
 
0.8%
56000 1
 
0.8%
69100 1
 
0.8%
78200 1
 
0.8%
107000 1
 
0.8%
150580 1
 
0.8%
ValueCountFrequency (%)
255183250 1
0.8%
145833580 1
0.8%
111597710 1
0.8%
63311870 1
0.8%
61998660 1
0.8%
43409120 1
0.8%
43290350 1
0.8%
34676420 1
0.8%
31927930 1
0.8%
31694180 1
0.8%

(입원)기관부담금(보험)
Real number (ℝ)

MISSING 

Distinct118
Distinct (%)100.0%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean2.1053494 × 108
Minimum-9.9324417 × 108
Maximum1.5051065 × 109
Zeros1
Zeros (%)0.8%
Negative6
Negative (%)5.0%
Memory size1.2 KiB
2023-12-13T03:09:30.978450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.9324417 × 108
5-th percentile-5989.5
Q186315720
median1.8240049 × 108
Q32.8720146 × 108
95-th percentile5.6144759 × 108
Maximum1.5051065 × 109
Range2.4983506 × 109
Interquartile range (IQR)2.0088574 × 108

Descriptive statistics

Standard deviation2.4871414 × 108
Coefficient of variation (CV)1.1813438
Kurtosis11.353001
Mean2.1053494 × 108
Median Absolute Deviation (MAD)1.0337915 × 108
Skewness1.0451765
Sum2.4843123 × 1010
Variance6.1858721 × 1016
MonotonicityNot monotonic
2023-12-13T03:09:31.135431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
815212510 1
 
0.8%
143440000 1
 
0.8%
19860000 1
 
0.8%
82919120 1
 
0.8%
429129890 1
 
0.8%
347278820 1
 
0.8%
179084260 1
 
0.8%
197783840 1
 
0.8%
163191040 1
 
0.8%
-993244170 1
 
0.8%
Other values (108) 108
90.0%
(Missing) 2
 
1.7%
ValueCountFrequency (%)
-993244170 1
0.8%
-963370 1
0.8%
-864370 1
0.8%
-189030 1
0.8%
-115920 1
0.8%
-39930 1
0.8%
0 1
0.8%
53530 1
0.8%
598320 1
0.8%
891730 1
0.8%
ValueCountFrequency (%)
1505106480 1
0.8%
1136969070 1
0.8%
856776080 1
0.8%
815212510 1
0.8%
701022290 1
0.8%
630213550 1
0.8%
549312420 1
0.8%
546805030 1
0.8%
480471990 1
0.8%
453813820 1
0.8%

(입원)기관부담금(급여)
Real number (ℝ)

MISSING  ZEROS 

Distinct112
Distinct (%)94.9%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean84315931
Minimum-33950
Maximum3.6740246 × 108
Zeros7
Zeros (%)5.8%
Negative1
Negative (%)0.8%
Memory size1.2 KiB
2023-12-13T03:09:31.353383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-33950
5-th percentile0
Q135114475
median85314015
Q31.0295069 × 108
95-th percentile1.9535744 × 108
Maximum3.6740246 × 108
Range3.6743641 × 108
Interquartile range (IQR)67836212

Descriptive statistics

Standard deviation64936730
Coefficient of variation (CV)0.77015967
Kurtosis3.0105956
Mean84315931
Median Absolute Deviation (MAD)26828040
Skewness1.2153153
Sum9.9492798 × 109
Variance4.2167789 × 1015
MonotonicityNot monotonic
2023-12-13T03:09:31.519541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
5.8%
305779070 1
 
0.8%
243638910 1
 
0.8%
170905530 1
 
0.8%
96696560 1
 
0.8%
102294320 1
 
0.8%
69518850 1
 
0.8%
53867140 1
 
0.8%
15703660 1
 
0.8%
11314970 1
 
0.8%
Other values (102) 102
85.0%
(Missing) 2
 
1.7%
ValueCountFrequency (%)
-33950 1
 
0.8%
0 7
5.8%
95800 1
 
0.8%
461060 1
 
0.8%
575560 1
 
0.8%
701420 1
 
0.8%
1613540 1
 
0.8%
3165350 1
 
0.8%
5480830 1
 
0.8%
6655100 1
 
0.8%
ValueCountFrequency (%)
367402460 1
0.8%
305779070 1
0.8%
243638910 1
0.8%
209460530 1
0.8%
209360970 1
0.8%
199365280 1
0.8%
194650170 1
0.8%
185834120 1
0.8%
183511650 1
0.8%
181290110 1
0.8%
Distinct118
Distinct (%)100.0%
Missing2
Missing (%)1.7%
Memory size1.1 KiB
2023-12-13T03:09:31.965715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.3135593
Min length1

Characters and Unicode

Total characters745
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)100.0%

Sample

1st row2576000
2nd row2328820
3rd row2950480
4th row2618180
5th row2408860
ValueCountFrequency (%)
2399890 1
 
0.8%
2624860 1
 
0.8%
29800 1
 
0.8%
420070 1
 
0.8%
2000 1
 
0.8%
25400 1
 
0.8%
495560 1
 
0.8%
1072480 1
 
0.8%
270220 1
 
0.8%
578600 1
 
0.8%
Other values (108) 108
91.5%
2023-12-13T03:09:32.571542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 185
24.8%
2 86
11.5%
1 69
 
9.3%
8 61
 
8.2%
6 61
 
8.2%
4 59
 
7.9%
7 59
 
7.9%
3 58
 
7.8%
5 55
 
7.4%
9 50
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 743
99.7%
Dash Punctuation 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 185
24.9%
2 86
11.6%
1 69
 
9.3%
8 61
 
8.2%
6 61
 
8.2%
4 59
 
7.9%
7 59
 
7.9%
3 58
 
7.8%
5 55
 
7.4%
9 50
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 745
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 185
24.8%
2 86
11.5%
1 69
 
9.3%
8 61
 
8.2%
6 61
 
8.2%
4 59
 
7.9%
7 59
 
7.9%
3 58
 
7.8%
5 55
 
7.4%
9 50
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 185
24.8%
2 86
11.5%
1 69
 
9.3%
8 61
 
8.2%
6 61
 
8.2%
4 59
 
7.9%
7 59
 
7.9%
3 58
 
7.8%
5 55
 
7.4%
9 50
 
6.7%

(외래)기관부담금(보험)
Real number (ℝ)

MISSING  ZEROS 

Distinct111
Distinct (%)94.1%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean20103701
Minimum-47120
Maximum5.2947223 × 108
Zeros8
Zeros (%)6.7%
Negative1
Negative (%)0.8%
Memory size1.2 KiB
2023-12-13T03:09:32.801769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-47120
5-th percentile0
Q11175767.5
median10390075
Q314362300
95-th percentile36154304
Maximum5.2947223 × 108
Range5.2951935 × 108
Interquartile range (IQR)13186532

Descriptive statistics

Standard deviation66232660
Coefficient of variation (CV)3.2945507
Kurtosis47.696398
Mean20103701
Median Absolute Deviation (MAD)6854835
Skewness6.7892326
Sum2.3722367 × 109
Variance4.3867653 × 1015
MonotonicityNot monotonic
2023-12-13T03:09:33.011713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
6.7%
263970 1
 
0.8%
2010000 1
 
0.8%
150000 1
 
0.8%
1655490 1
 
0.8%
13496440 1
 
0.8%
21828080 1
 
0.8%
9655330 1
 
0.8%
829870 1
 
0.8%
10331630 1
 
0.8%
Other values (101) 101
84.2%
(Missing) 2
 
1.7%
ValueCountFrequency (%)
-47120 1
 
0.8%
0 8
6.7%
3000 1
 
0.8%
9200 1
 
0.8%
50200 1
 
0.8%
60000 1
 
0.8%
97930 1
 
0.8%
100000 1
 
0.8%
125800 1
 
0.8%
130000 1
 
0.8%
ValueCountFrequency (%)
529472230 1
0.8%
468689850 1
0.8%
187391840 1
0.8%
60187740 1
0.8%
55966640 1
0.8%
40358710 1
0.8%
35412350 1
0.8%
35340710 1
0.8%
30709390 1
0.8%
29382030 1
0.8%

(외래)기관부담금(급여)
Real number (ℝ)

MISSING  ZEROS 

Distinct105
Distinct (%)90.5%
Missing4
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean5507254.2
Minimum-143700
Maximum1.5051019 × 108
Zeros10
Zeros (%)8.3%
Negative2
Negative (%)1.7%
Memory size1.2 KiB
2023-12-13T03:09:33.190052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-143700
5-th percentile0
Q1202637.5
median2567045
Q35106122.5
95-th percentile14684905
Maximum1.5051019 × 108
Range1.5065389 × 108
Interquartile range (IQR)4903485

Descriptive statistics

Standard deviation15555683
Coefficient of variation (CV)2.8245804
Kurtosis68.806071
Mean5507254.2
Median Absolute Deviation (MAD)2428665
Skewness7.8122841
Sum6.3884149 × 108
Variance2.4197926 × 1014
MonotonicityNot monotonic
2023-12-13T03:09:33.376787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
8.3%
53500 2
 
1.7%
1500 2
 
1.7%
149200 1
 
0.8%
2304150 1
 
0.8%
4220440 1
 
0.8%
2508100 1
 
0.8%
1584880 1
 
0.8%
9000 1
 
0.8%
2243520 1
 
0.8%
Other values (95) 95
79.2%
(Missing) 4
 
3.3%
ValueCountFrequency (%)
-143700 1
 
0.8%
-49810 1
 
0.8%
0 10
8.3%
1500 2
 
1.7%
6000 1
 
0.8%
8500 1
 
0.8%
9000 1
 
0.8%
11450 1
 
0.8%
11530 1
 
0.8%
13140 1
 
0.8%
ValueCountFrequency (%)
150510190 1
0.8%
69773370 1
0.8%
26208840 1
0.8%
20215520 1
0.8%
16122780 1
0.8%
14887540 1
0.8%
14617360 1
0.8%
13793070 1
0.8%
12840700 1
0.8%
11509960 1
0.8%

토지 및 건물대여료
Real number (ℝ)

MISSING  ZEROS 

Distinct116
Distinct (%)98.3%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean6170097.5
Minimum-4681500
Maximum62052820
Zeros2
Zeros (%)1.7%
Negative13
Negative (%)10.8%
Memory size1.2 KiB
2023-12-13T03:09:33.600929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4681500
5-th percentile-1887747.5
Q1780155
median1571000
Q36358757.5
95-th percentile23305756
Maximum62052820
Range66734320
Interquartile range (IQR)5578602.5

Descriptive statistics

Standard deviation10532746
Coefficient of variation (CV)1.7070631
Kurtosis10.617674
Mean6170097.5
Median Absolute Deviation (MAD)1805005
Skewness2.8935158
Sum7.2807151 × 108
Variance1.1093874 × 1014
MonotonicityNot monotonic
2023-12-13T03:09:33.793836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
1.7%
1045720 2
 
1.7%
6204310 1
 
0.8%
17294820 1
 
0.8%
23065370 1
 
0.8%
21354530 1
 
0.8%
2949370 1
 
0.8%
-119110 1
 
0.8%
6111330 1
 
0.8%
985680 1
 
0.8%
Other values (106) 106
88.3%
(Missing) 2
 
1.7%
ValueCountFrequency (%)
-4681500 1
0.8%
-3689710 1
0.8%
-3122650 1
0.8%
-2702020 1
0.8%
-2469580 1
0.8%
-2038070 1
0.8%
-1861220 1
0.8%
-1298810 1
0.8%
-975160 1
0.8%
-697850 1
0.8%
ValueCountFrequency (%)
62052820 1
0.8%
54738600 1
0.8%
45905530 1
0.8%
25111300 1
0.8%
24210900 1
0.8%
23755330 1
0.8%
23226420 1
0.8%
23065370 1
0.8%
22169750 1
0.8%
22011420 1
0.8%

면허료 및 수수료
Real number (ℝ)

MISSING  ZEROS 

Distinct86
Distinct (%)72.9%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean319406.1
Minimum-7241000
Maximum7818740
Zeros33
Zeros (%)27.5%
Negative1
Negative (%)0.8%
Memory size1.2 KiB
2023-12-13T03:09:33.985722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7241000
5-th percentile0
Q10
median375745
Q3475697.5
95-th percentile685509.5
Maximum7818740
Range15059740
Interquartile range (IQR)475697.5

Descriptive statistics

Standard deviation1014230.6
Coefficient of variation (CV)3.1753638
Kurtosis51.602892
Mean319406.1
Median Absolute Deviation (MAD)140620
Skewness-0.084116635
Sum37689920
Variance1.0286636 × 1012
MonotonicityNot monotonic
2023-12-13T03:09:34.190078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
27.5%
526650 1
 
0.8%
376280 1
 
0.8%
368110 1
 
0.8%
325560 1
 
0.8%
467030 1
 
0.8%
543080 1
 
0.8%
396930 1
 
0.8%
517620 1
 
0.8%
484890 1
 
0.8%
Other values (76) 76
63.3%
(Missing) 2
 
1.7%
ValueCountFrequency (%)
-7241000 1
 
0.8%
0 33
27.5%
41000 1
 
0.8%
53600 1
 
0.8%
124200 1
 
0.8%
171900 1
 
0.8%
233550 1
 
0.8%
266910 1
 
0.8%
281450 1
 
0.8%
285700 1
 
0.8%
ValueCountFrequency (%)
7818740 1
0.8%
1103170 1
0.8%
934900 1
0.8%
850270 1
0.8%
764240 1
0.8%
692420 1
0.8%
684290 1
0.8%
663150 1
0.8%
657810 1
0.8%
638570 1
0.8%

이월금(잉여금) 및 전입금
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)22.2%
Missing3
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1.4544613 × 109
Minimum-80000000
Maximum1.8009 × 1010
Zeros91
Zeros (%)75.8%
Negative2
Negative (%)1.7%
Memory size1.2 KiB
2023-12-13T03:09:34.376481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-80000000
5-th percentile0
Q10
median0
Q30
95-th percentile9.4776236 × 109
Maximum1.8009 × 1010
Range1.8089 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.4439371 × 109
Coefficient of variation (CV)2.3678437
Kurtosis6.4167314
Mean1.4544613 × 109
Median Absolute Deviation (MAD)0
Skewness2.5623668
Sum1.7017197 × 1011
Variance1.1860703 × 1019
MonotonicityNot monotonic
2023-12-13T03:09:34.530657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 91
75.8%
5346800000 2
 
1.7%
12007000000 1
 
0.8%
9070200000 1
 
0.8%
1007800000 1
 
0.8%
6088553250 1
 
0.8%
-52000000 1
 
0.8%
2673400000 1
 
0.8%
5946288260 1
 
0.8%
7861000000 1
 
0.8%
Other values (16) 16
 
13.3%
(Missing) 3
 
2.5%
ValueCountFrequency (%)
-80000000 1
 
0.8%
-52000000 1
 
0.8%
0 91
75.8%
7197000 1
 
0.8%
1007800000 1
 
0.8%
1589920620 1
 
0.8%
2673400000 1
 
0.8%
3208173350 1
 
0.8%
3400000000 1
 
0.8%
4530137620 1
 
0.8%
ValueCountFrequency (%)
18009000000 1
0.8%
14063000000 1
0.8%
12007000000 1
0.8%
10691000000 1
0.8%
9570314970 1
0.8%
9492000000 1
0.8%
9474029440 1
0.8%
9070200000 1
0.8%
9006256000 1
0.8%
8802000000 1
0.8%

기타수입
Real number (ℝ)

MISSING  ZEROS 

Distinct108
Distinct (%)91.5%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean87720174
Minimum-50332590
Maximum1.5211011 × 109
Zeros11
Zeros (%)9.2%
Negative4
Negative (%)3.3%
Memory size1.2 KiB
2023-12-13T03:09:34.702811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-50332590
5-th percentile0
Q1164070
median2909510
Q369210145
95-th percentile5.3145167 × 108
Maximum1.5211011 × 109
Range1.5714337 × 109
Interquartile range (IQR)69046075

Descriptive statistics

Standard deviation2.2392888 × 108
Coefficient of variation (CV)2.5527638
Kurtosis18.891727
Mean87720174
Median Absolute Deviation (MAD)2909510
Skewness4.0347699
Sum1.0350981 × 1010
Variance5.0144146 × 1016
MonotonicityNot monotonic
2023-12-13T03:09:34.888258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
9.2%
2022700 1
 
0.8%
1065409410 1
 
0.8%
590199540 1
 
0.8%
529129480 1
 
0.8%
110400000 1
 
0.8%
596174610 1
 
0.8%
-15338230 1
 
0.8%
67857580 1
 
0.8%
126562860 1
 
0.8%
Other values (98) 98
81.7%
(Missing) 2
 
1.7%
ValueCountFrequency (%)
-50332590 1
 
0.8%
-15338230 1
 
0.8%
-4455280 1
 
0.8%
-818740 1
 
0.8%
0 11
9.2%
4550 1
 
0.8%
8080 1
 
0.8%
8220 1
 
0.8%
12910 1
 
0.8%
14670 1
 
0.8%
ValueCountFrequency (%)
1521101080 1
0.8%
1065409410 1
0.8%
1015674070 1
0.8%
596174610 1
0.8%
590199540 1
0.8%
544610750 1
0.8%
529129480 1
0.8%
453865360 1
0.8%
447699780 1
0.8%
388708840 1
0.8%

Interactions

2023-12-13T03:09:28.847212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:21.196390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:22.006825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:23.013360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:24.101717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:25.150669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:26.609751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.428723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.112907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.919245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:21.274451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:22.112384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:23.152217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:24.192656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:25.572928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:26.729723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.505537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.186319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.991877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:21.353186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:22.215537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:23.299833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:24.296412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:25.694171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:26.844590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.581577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.261236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:29.068017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:21.443570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:22.319308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:23.420980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:24.416722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:25.841807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:26.948117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.664976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.350629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:29.143199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:21.536358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:22.413267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:23.529912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:24.517327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:25.964678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.025512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.734126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.428467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:29.219001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:21.641639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:22.512262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:23.631743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:24.638883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:26.067896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.103017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.810106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.516443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:29.293868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:21.731298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:22.623368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:23.730437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:24.766233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:26.193762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.181024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.882129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.607549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:29.371901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:21.820596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:22.740094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:23.845962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:24.885587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:26.327187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.263541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.957160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.691874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:29.465980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:21.909892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:22.899122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:23.984332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:25.044998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:26.464664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:27.350661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.037209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:28.766882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:09:35.012357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(입원)본인부담금(입원)기관부담금(보험)(입원)기관부담금(급여)(외래)기관부담금(보험)(외래)기관부담금(급여)토지 및 건물대여료면허료 및 수수료이월금(잉여금) 및 전입금기타수입
(입원)본인부담금1.0000.0000.0000.0000.0000.0000.3880.0000.606
(입원)기관부담금(보험)0.0001.0000.0000.0000.0000.0000.1350.2380.661
(입원)기관부담금(급여)0.0000.0001.0000.0700.4510.2760.5070.8240.441
(외래)기관부담금(보험)0.0000.0000.0701.0000.6280.3580.0000.4010.000
(외래)기관부담금(급여)0.0000.0000.4510.6281.0000.6170.0000.3810.000
토지 및 건물대여료0.0000.0000.2760.3580.6171.0000.8070.2490.338
면허료 및 수수료0.3880.1350.5070.0000.0000.8071.0000.0000.380
이월금(잉여금) 및 전입금0.0000.2380.8240.4010.3810.2490.0001.0000.000
기타수입0.6060.6610.4410.0000.0000.3380.3800.0001.000
2023-12-13T03:09:35.166171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(입원)본인부담금(입원)기관부담금(보험)(입원)기관부담금(급여)(외래)기관부담금(보험)(외래)기관부담금(급여)토지 및 건물대여료면허료 및 수수료이월금(잉여금) 및 전입금기타수입
(입원)본인부담금1.000-0.021-0.187-0.199-0.3230.163-0.354-0.0390.312
(입원)기관부담금(보험)-0.0211.0000.1520.1500.1010.0220.0140.0880.152
(입원)기관부담금(급여)-0.1870.1521.0000.3230.4900.1810.2930.184-0.175
(외래)기관부담금(보험)-0.1990.1500.3231.0000.4010.0790.470-0.023-0.187
(외래)기관부담금(급여)-0.3230.1010.4900.4011.0000.0390.4540.098-0.196
토지 및 건물대여료0.1630.0220.1810.0790.0391.0000.0210.0730.117
면허료 및 수수료-0.3540.0140.2930.4700.4540.0211.0000.004-0.379
이월금(잉여금) 및 전입금-0.0390.0880.184-0.0230.0980.0730.0041.0000.015
기타수입0.3120.152-0.175-0.187-0.1960.117-0.3790.0151.000

Missing values

2023-12-13T03:09:29.590111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:09:29.723244image/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.
2023-12-13T03:09:29.849991image/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

년월(입원)본인부담금(입원)기관부담금(보험)(입원)기관부담금(급여)(외래)본인부담금(외래)기관부담금(보험)(외래)기관부담금(급여)토지 및 건물대여료면허료 및 수수료이월금(잉여금) 및 전입금기타수입
02014-01161053041998201749202302576000300049343206130080434200900625600069661000
12014-021432350180555440961675702328820151997101864160147692006135400269270
22014-0311985601745889401026404502950480173418502807780202299406385700402140
32014-0412305000026181801250798053500-246958056617008080
42014-05113660022972146017174848024088601382070642292069212068429003004660
52014-061210630181420860897215902727160160022405750062663006578100155700
62014-0711286001833801201614844902220110160150007460150237553306924200263560
72014-081161310189632750709591902259290148092504048580062367003009710
82014-091298240-115920908434101966000158676904299770767644085027002024000
92014-101324580366697500826081202046610143751404059280-69785066315002814360
년월(입원)본인부담금(입원)기관부담금(보험)(입원)기관부담금(급여)(외래)본인부담금(외래)기관부담금(보험)(외래)기관부담금(급여)토지 및 건물대여료면허료 및 수수료이월금(잉여금) 및 전입금기타수입
1102023-03437059018081699087752370816850489510020353306205282000110000000
1112023-045600011848000002463004867302039130-468150000100000000
1122023-0515555301618787301946501703115309163570138886037310400035367940
1132023-06864117014523945097902140844980242855302135890651161000447699780
1142023-0715075401427764807542721018230012931590161281088980000
1152023-08646223011886988067594720254600812119013385901004620005138000
1162023-09359247016643279095826390406340763200692090081279900029602120
1172023-10181220019641036071347300127796504235401303720240820-724100000
118<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
119<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

년월(입원)본인부담금(입원)기관부담금(보험)(입원)기관부담금(급여)(외래)본인부담금(외래)기관부담금(보험)(외래)기관부담금(급여)토지 및 건물대여료면허료 및 수수료이월금(잉여금) 및 전입금기타수입# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2