Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory947.3 KiB
Average record size in memory97.0 B

Variable types

DateTime1
Numeric7
Categorical2

Dataset

Description컬럼명 구분 처리년월, 시도보호기호, 시군구보호기호, 의료급여종별코드, 요양기관종별코드, 진료실인원, 진료건수, 심사결정총진료비, 심사결정기관부담금, 심사결정본인부담금
URLhttps://www.data.go.kr/data/15121257/fileData.do

Alerts

시도보호기호 is highly overall correlated with 시군구보호기호High correlation
시군구보호기호 is highly overall correlated with 시도보호기호High correlation
진료실인원 is highly overall correlated with 진료건수 and 3 other fieldsHigh correlation
진료건수 is highly overall correlated with 진료실인원 and 3 other fieldsHigh correlation
심사결정총진료비 is highly overall correlated with 진료실인원 and 3 other fieldsHigh correlation
심사결정기관부담금 is highly overall correlated with 진료실인원 and 3 other fieldsHigh correlation
심사결정본인부담금 is highly overall correlated with 진료실인원 and 3 other fieldsHigh correlation
의료급여종별코드 is highly imbalanced (67.8%)Imbalance
심사결정총진료비 has 123 (1.2%) zerosZeros
심사결정기관부담금 has 123 (1.2%) zerosZeros
심사결정본인부담금 has 200 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-12 21:42:18.060628
Analysis finished2023-12-12 21:42:25.765704
Duration7.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처리년월
Date

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-01-01 00:00:00
Maximum2022-12-01 00:00:00
2023-12-13T06:42:25.838319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:26.019331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시도보호기호
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6345470.3
Minimum5691000
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:42:26.205040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5691000
5-th percentile6110000
Q16270000
median6410000
Q36450000
95-th percentile6480000
Maximum6500000
Range809000
Interquartile range (IQR)180000

Descriptive statistics

Standard deviation129687.47
Coefficient of variation (CV)0.020437803
Kurtosis1.0529333
Mean6345470.3
Median Absolute Deviation (MAD)70000
Skewness-1.0606363
Sum6.3454703 × 1010
Variance1.6818841 × 1010
MonotonicityNot monotonic
2023-12-13T06:42:26.356113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6410000 1745
17.4%
6110000 1484
14.8%
6260000 878
8.8%
6470000 788
7.9%
6480000 692
 
6.9%
6460000 668
 
6.7%
6280000 558
 
5.6%
6270000 507
 
5.1%
6440000 492
 
4.9%
6420000 444
 
4.4%
Other values (7) 1744
17.4%
ValueCountFrequency (%)
5691000 33
 
0.3%
6110000 1484
14.8%
6260000 878
8.8%
6270000 507
 
5.1%
6280000 558
 
5.6%
6290000 311
 
3.1%
6300000 278
 
2.8%
6310000 187
 
1.9%
6410000 1745
17.4%
6420000 444
 
4.4%
ValueCountFrequency (%)
6500000 141
 
1.4%
6480000 692
 
6.9%
6470000 788
7.9%
6460000 668
 
6.7%
6450000 428
 
4.3%
6440000 492
 
4.9%
6430000 366
 
3.7%
6420000 444
 
4.4%
6410000 1745
17.4%
6310000 187
 
1.9%

시군구보호기호
Real number (ℝ)

HIGH CORRELATION 

Distinct245
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4151164
Minimum3000000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:42:26.506307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3080000
Q13430000
median3980000
Q34830000
95-th percentile5670123
Maximum6520000
Range3520000
Interquartile range (IQR)1400000

Descriptive statistics

Standard deviation847039.63
Coefficient of variation (CV)0.2040487
Kurtosis-0.66534128
Mean4151164
Median Absolute Deviation (MAD)660000
Skewness0.55497507
Sum4.151164 × 1010
Variance7.1747614 × 1011
MonotonicityNot monotonic
2023-12-13T06:42:26.704712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3860000 98
 
1.0%
5350000 96
 
1.0%
3100000 96
 
1.0%
3780000 94
 
0.9%
3060000 90
 
0.9%
3540000 88
 
0.9%
3620000 88
 
0.9%
4490000 87
 
0.9%
4060000 85
 
0.9%
3530000 84
 
0.8%
Other values (235) 9094
90.9%
ValueCountFrequency (%)
3000000 40
0.4%
3010000 34
 
0.3%
3020000 53
0.5%
3030000 60
0.6%
3040000 50
0.5%
3050000 69
0.7%
3060000 90
0.9%
3070000 74
0.7%
3080000 77
0.8%
3090000 53
0.5%
ValueCountFrequency (%)
6520000 59
0.6%
6510000 82
0.8%
5735000 32
 
0.3%
5730000 22
 
0.2%
5725000 48
0.5%
5720000 33
0.3%
5700000 25
 
0.2%
5690000 33
0.3%
5680000 34
0.3%
5670206 30
 
0.3%

의료급여종별코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9413 
2
 
587

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9413
94.1%
2 587
 
5.9%

Length

2023-12-13T06:42:26.901956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:42:27.021788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9413
94.1%
2 587
 
5.9%

요양기관종별코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
5
7388 
4
1092 
1
1030 
2
 
264
0
 
226

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row1
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 7388
73.9%
4 1092
 
10.9%
1 1030
 
10.3%
2 264
 
2.6%
0 226
 
2.3%

Length

2023-12-13T06:42:27.141355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:42:27.274081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 7388
73.9%
4 1092
 
10.9%
1 1030
 
10.3%
2 264
 
2.6%
0 226
 
2.3%

진료실인원
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1581
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:42:27.391652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum19
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.808544
Coefficient of variation (CV)0.83802605
Kurtosis6.8036604
Mean2.1581
Median Absolute Deviation (MAD)0
Skewness2.2826015
Sum21581
Variance3.2708315
MonotonicityNot monotonic
2023-12-13T06:42:27.547669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 5337
53.4%
2 1960
 
19.6%
3 1035
 
10.3%
4 636
 
6.4%
5 412
 
4.1%
6 243
 
2.4%
7 151
 
1.5%
8 96
 
1.0%
9 56
 
0.6%
10 37
 
0.4%
Other values (7) 37
 
0.4%
ValueCountFrequency (%)
1 5337
53.4%
2 1960
 
19.6%
3 1035
 
10.3%
4 636
 
6.4%
5 412
 
4.1%
6 243
 
2.4%
7 151
 
1.5%
8 96
 
1.0%
9 56
 
0.6%
10 37
 
0.4%
ValueCountFrequency (%)
19 1
 
< 0.1%
16 2
 
< 0.1%
15 1
 
< 0.1%
14 3
 
< 0.1%
13 4
 
< 0.1%
12 12
 
0.1%
11 14
 
0.1%
10 37
 
0.4%
9 56
0.6%
8 96
1.0%

진료건수
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0467
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:42:27.695143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile13
Maximum54
Range53
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1302573
Coefficient of variation (CV)1.0206482
Kurtosis8.1685509
Mean4.0467
Median Absolute Deviation (MAD)2
Skewness2.3019096
Sum40467
Variance17.059025
MonotonicityNot monotonic
2023-12-13T06:42:27.847831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 3539
35.4%
2 1412
 
14.1%
3 1083
 
10.8%
4 852
 
8.5%
5 741
 
7.4%
6 466
 
4.7%
7 370
 
3.7%
8 304
 
3.0%
9 257
 
2.6%
10 188
 
1.9%
Other values (26) 788
 
7.9%
ValueCountFrequency (%)
1 3539
35.4%
2 1412
 
14.1%
3 1083
 
10.8%
4 852
 
8.5%
5 741
 
7.4%
6 466
 
4.7%
7 370
 
3.7%
8 304
 
3.0%
9 257
 
2.6%
10 188
 
1.9%
ValueCountFrequency (%)
54 1
 
< 0.1%
37 1
 
< 0.1%
36 1
 
< 0.1%
34 1
 
< 0.1%
33 2
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 4
< 0.1%
27 2
< 0.1%

심사결정총진료비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5334
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1653525.5
Minimum0
Maximum21813670
Zeros123
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:42:28.044053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile259470
Q1493860
median1066290
Q32257397.5
95-th percentile5233492.5
Maximum21813670
Range21813670
Interquartile range (IQR)1763537.5

Descriptive statistics

Standard deviation1710065.4
Coefficient of variation (CV)1.0341936
Kurtosis7.9628412
Mean1653525.5
Median Absolute Deviation (MAD)698755
Skewness2.2566712
Sum1.6535255 × 1010
Variance2.9243238 × 1012
MonotonicityNot monotonic
2023-12-13T06:42:28.232379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
271410 130
 
1.3%
0 123
 
1.2%
267430 122
 
1.2%
259470 111
 
1.1%
277520 106
 
1.1%
254270 86
 
0.9%
247540 76
 
0.8%
542820 62
 
0.6%
555040 60
 
0.6%
534860 44
 
0.4%
Other values (5324) 9080
90.8%
ValueCountFrequency (%)
0 123
1.2%
58540 1
 
< 0.1%
62000 1
 
< 0.1%
62530 1
 
< 0.1%
65760 1
 
< 0.1%
65970 1
 
< 0.1%
92690 1
 
< 0.1%
99690 1
 
< 0.1%
130050 1
 
< 0.1%
138920 1
 
< 0.1%
ValueCountFrequency (%)
21813670 1
< 0.1%
15330170 1
< 0.1%
15136690 1
< 0.1%
15032390 1
< 0.1%
14891010 1
< 0.1%
13636700 1
< 0.1%
13130670 1
< 0.1%
13006540 1
< 0.1%
12450860 1
< 0.1%
11916260 1
< 0.1%

심사결정기관부담금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5857
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1534404.6
Minimum0
Maximum20723160
Zeros123
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:42:28.404544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile235900
Q1437160
median971660
Q32091570
95-th percentile4849598
Maximum20723160
Range20723160
Interquartile range (IQR)1654410

Descriptive statistics

Standard deviation1599099.9
Coefficient of variation (CV)1.0421632
Kurtosis8.3925644
Mean1534404.6
Median Absolute Deviation (MAD)641925
Skewness2.2983936
Sum1.5344046 × 1010
Variance2.5571206 × 1012
MonotonicityNot monotonic
2023-12-13T06:42:28.854223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 123
 
1.2%
254060 109
 
1.1%
257840 105
 
1.1%
246500 102
 
1.0%
263650 95
 
0.9%
241560 73
 
0.7%
515680 51
 
0.5%
198040 45
 
0.4%
266070 41
 
0.4%
269860 40
 
0.4%
Other values (5847) 9216
92.2%
ValueCountFrequency (%)
0 123
1.2%
50030 1
 
< 0.1%
55620 1
 
< 0.1%
58900 1
 
< 0.1%
62480 1
 
< 0.1%
62680 1
 
< 0.1%
74160 1
 
< 0.1%
94710 1
 
< 0.1%
112530 1
 
< 0.1%
123550 1
 
< 0.1%
ValueCountFrequency (%)
20723160 1
< 0.1%
14563800 1
< 0.1%
14380000 1
< 0.1%
14280860 1
< 0.1%
14146640 1
< 0.1%
12955040 1
< 0.1%
12356320 1
< 0.1%
12190270 1
< 0.1%
11828420 1
< 0.1%
11320560 1
< 0.1%

심사결정본인부담금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5280
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118638.08
Minimum0
Maximum2191260
Zeros200
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:42:28.992615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12970
Q127750
median65540
Q3138860
95-th percentile400040
Maximum2191260
Range2191260
Interquartile range (IQR)111110

Descriptive statistics

Standard deviation165361.52
Coefficient of variation (CV)1.3938317
Kurtosis25.095672
Mean118638.08
Median Absolute Deviation (MAD)45180
Skewness4.1400696
Sum1.1863808 × 109
Variance2.7344431 × 1010
MonotonicityNot monotonic
2023-12-13T06:42:29.138555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 200
 
2.0%
13370 109
 
1.1%
13570 105
 
1.1%
12970 102
 
1.0%
13870 95
 
0.9%
12710 73
 
0.7%
27140 51
 
0.5%
49500 45
 
0.4%
14000 39
 
0.4%
14200 38
 
0.4%
Other values (5270) 9143
91.4%
ValueCountFrequency (%)
0 200
2.0%
1400 1
 
< 0.1%
2920 1
 
< 0.1%
3280 1
 
< 0.1%
3290 1
 
< 0.1%
4980 1
 
< 0.1%
6500 1
 
< 0.1%
6940 1
 
< 0.1%
7040 1
 
< 0.1%
7990 1
 
< 0.1%
ValueCountFrequency (%)
2191260 1
< 0.1%
1882880 1
< 0.1%
1844940 1
< 0.1%
1801430 1
< 0.1%
1624880 1
< 0.1%
1613140 1
< 0.1%
1598490 1
< 0.1%
1564470 1
< 0.1%
1562200 1
< 0.1%
1548930 1
< 0.1%

Interactions

2023-12-13T06:42:24.739618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:19.858172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:20.745271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.550013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.542446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.265893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.970711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.853586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:19.986833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:20.902595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.660278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.656744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.393547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.092332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.953103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:20.096694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.032298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.772470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.765339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.519561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.204667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:25.056230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:20.209342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.136991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.861870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.859576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.612651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.318919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:25.163615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:20.324651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.236917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.246017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.946808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.700319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.426440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:25.254806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:20.456856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.330667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.341045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.052131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.791154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.536590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:25.361801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:20.594665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.444444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.442111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.158164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.881226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.623824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:42:29.229117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리년월시도보호기호시군구보호기호의료급여종별코드요양기관종별코드진료실인원진료건수심사결정총진료비심사결정기관부담금심사결정본인부담금
처리년월1.0000.0000.0000.0000.0610.0400.0860.0740.0920.451
시도보호기호0.0001.0000.9120.0840.1010.1850.1310.0720.0770.029
시군구보호기호0.0000.9121.0000.0860.1480.1890.1400.1790.1800.091
의료급여종별코드0.0000.0840.0861.0000.0620.1930.1550.1130.1280.023
요양기관종별코드0.0610.1010.1480.0621.0000.3630.2190.2370.2320.212
진료실인원0.0400.1850.1890.1930.3631.0000.8310.8220.8260.572
진료건수0.0860.1310.1400.1550.2190.8311.0000.8850.8720.569
심사결정총진료비0.0740.0720.1790.1130.2370.8220.8851.0000.9990.687
심사결정기관부담금0.0920.0770.1800.1280.2320.8260.8720.9991.0000.632
심사결정본인부담금0.4510.0290.0910.0230.2120.5720.5690.6870.6321.000
2023-12-13T06:42:29.350073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의료급여종별코드요양기관종별코드
의료급여종별코드1.0000.075
요양기관종별코드0.0751.000
2023-12-13T06:42:29.447162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도보호기호시군구보호기호진료실인원진료건수심사결정총진료비심사결정기관부담금심사결정본인부담금의료급여종별코드요양기관종별코드
시도보호기호1.0000.939-0.142-0.046-0.048-0.047-0.0480.0550.071
시군구보호기호0.9391.000-0.159-0.068-0.070-0.070-0.0710.0860.085
진료실인원-0.142-0.1591.0000.8170.7510.7530.6490.1480.159
진료건수-0.046-0.0680.8171.0000.9170.9130.8290.1160.136
심사결정총진료비-0.048-0.0700.7510.9171.0000.9980.8840.1130.139
심사결정기관부담금-0.047-0.0700.7530.9130.9981.0000.8560.1280.135
심사결정본인부담금-0.048-0.0710.6490.8290.8840.8561.0000.0180.090
의료급여종별코드0.0550.0860.1480.1160.1130.1280.0181.0000.075
요양기관종별코드0.0710.0850.1590.1360.1390.1350.0900.0751.000

Missing values

2023-12-13T06:42:25.506591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:42:25.677383image/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

처리년월시도보호기호시군구보호기호의료급여종별코드요양기관종별코드진료실인원진료건수심사결정총진료비심사결정기관부담금심사결정본인부담금
89602021-06642000041900001511000
119062019-1164600004990000151146015043715023000
80632021-0661100003090000111156014053214028000
106982020-1164500004650000153480229076218040110
136292022-0562600003330000141196436091615048210
56212020-1061100003030000153630753902921650153740
150862020-07628000035105001561658399805548020291960
31342018-0564500004680000141128744027307014370
136232022-0562600003300000151146208043898023100
147962020-0564800005330000153840147703814060200710
처리년월시도보호기호시군구보호기호의료급여종별코드요양기관종별코드진료실인원진료건수심사결정총진료비심사결정기관부담금심사결정본인부담금
142252019-1264600004950000151178932074986039460
150392019-0264800005350000151198147093240049070
126232022-0664800005400000151146208043898023100
105732020-066410000390000015331472370139876073610
80262021-0464100005540000151127141025784013570
11632017-0362700003480000151127437021950054870
116182022-0564800005670123154423117602196190115570
88542019-1062600003300000141154150051443027070
65632021-05626000033500001571765211506195160325990
97602019-0364400004510000151146015043715023000