Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory937.5 KiB
Average record size in memory96.0 B

Variable types

Categorical4
Numeric6

Dataset

Descriptiono (내용) 시군구별 일반(구강)검진 대상자 및 수검자 인원수 o (변수 레이아웃) 1 기준일자 2 사업년도 3 통계유형구분코드(11: 일반, 21: 의료급여생애) 4 시도코드 5 시군구코드 6 직역코드(E: 의료급여, G: 공교, J: 지역, K: 직장) 7 가입자구분코드(1: 지역세대주, 2: 지역세대원, 3: 비가입세대주, 4: 임의계속사업자, 5: 직장가입자, 6: 직장피부양자, 7: 의료급여세대주, 8: 의료급여세대원, 9: 임의계속피부양자) 8 일반대상자인원수 9 일반수검자인원수 10 구강수검자인원수 o (자료제공범위) 조회일자 기준 최근 ‘1개월’ (2023년7월28일~2023년8월28일)
URLhttps://www.data.go.kr/data/15121964/fileData.do

Alerts

사업년도 has constant value ""Constant
가입자구분코드 is highly overall correlated with 통계유형구분코드 and 1 other fieldsHigh correlation
일반대상자인원수 is highly overall correlated with 일반수검자인원수 and 1 other fieldsHigh correlation
일반수검자인원수 is highly overall correlated with 일반대상자인원수 and 1 other fieldsHigh correlation
구강수검자인원수 is highly overall correlated with 일반대상자인원수 and 1 other fieldsHigh correlation
통계유형구분코드 is highly overall correlated with 가입자구분코드 and 1 other fieldsHigh correlation
직역코드 is highly overall correlated with 가입자구분코드 and 1 other fieldsHigh correlation
일반대상자인원수 has 1589 (15.9%) zerosZeros
일반수검자인원수 has 1608 (16.1%) zerosZeros
구강수검자인원수 has 1965 (19.7%) zerosZeros

Reproduction

Analysis started2023-12-12 16:09:23.665663
Analysis finished2023-12-12 16:09:29.009580
Duration5.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일자
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-08-16
 
358
2023-07-28
 
335
2023-08-17
 
335
2023-08-04
 
334
2023-08-22
 
330
Other values (27)
8308 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-25
2nd row2023-08-15
3rd row2023-08-05
4th row2023-08-03
5th row2023-07-29

Common Values

ValueCountFrequency (%)
2023-08-16 358
 
3.6%
2023-07-28 335
 
3.4%
2023-08-17 335
 
3.4%
2023-08-04 334
 
3.3%
2023-08-22 330
 
3.3%
2023-08-10 329
 
3.3%
2023-08-11 327
 
3.3%
2023-08-19 323
 
3.2%
2023-08-05 322
 
3.2%
2023-08-18 322
 
3.2%
Other values (22) 6685
66.8%

Length

2023-12-13T01:09:29.082779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-08-16 358
 
3.6%
2023-07-28 335
 
3.4%
2023-08-17 335
 
3.4%
2023-08-04 334
 
3.3%
2023-08-22 330
 
3.3%
2023-08-10 329
 
3.3%
2023-08-11 327
 
3.3%
2023-08-19 323
 
3.2%
2023-08-05 322
 
3.2%
2023-08-18 322
 
3.2%
Other values (22) 6685
66.8%

사업년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 10000
100.0%

Length

2023-12-13T01:09:29.228799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:09:29.338016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 10000
100.0%

통계유형구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
11
8411 
21
1589 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 8411
84.1%
21 1589
 
15.9%

Length

2023-12-13T01:09:29.431278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:09:29.521659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 8411
84.1%
21 1589
 
15.9%

시도코드
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.2725
Minimum11
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:09:29.625088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q129
median43
Q346
95-th percentile51
Maximum51
Range40
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.564031
Coefficient of variation (CV)0.30214988
Kurtosis0.36014755
Mean38.2725
Median Absolute Deviation (MAD)4
Skewness-1.1957707
Sum382725
Variance133.72682
MonotonicityNot monotonic
2023-12-13T01:09:29.774657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
41 1623
16.2%
11 970
9.7%
47 865
8.6%
46 856
8.6%
48 856
8.6%
44 656
 
6.6%
51 655
 
6.6%
45 613
 
6.1%
26 600
 
6.0%
43 549
 
5.5%
Other values (8) 1757
17.6%
ValueCountFrequency (%)
11 970
9.7%
26 600
 
6.0%
27 361
 
3.6%
28 436
 
4.4%
29 201
 
2.0%
30 196
 
2.0%
31 212
 
2.1%
36 41
 
0.4%
41 1623
16.2%
42 235
 
2.4%
ValueCountFrequency (%)
51 655
6.6%
50 75
 
0.8%
48 856
8.6%
47 865
8.6%
46 856
8.6%
45 613
 
6.1%
44 656
6.6%
43 549
 
5.5%
42 235
 
2.4%
41 1623
16.2%

시군구코드
Real number (ℝ)

Distinct106
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean435.9325
Minimum110
Maximum940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:09:29.922918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile111
Q1170
median320
Q3740
95-th percentile860
Maximum940
Range830
Interquartile range (IQR)570

Descriptive statistics

Standard deviation282.11995
Coefficient of variation (CV)0.64716429
Kurtosis-1.5787847
Mean435.9325
Median Absolute Deviation (MAD)197
Skewness0.30473081
Sum4359325
Variance79591.665
MonotonicityNot monotonic
2023-12-13T01:09:30.060280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 441
 
4.4%
170 408
 
4.1%
710 338
 
3.4%
140 327
 
3.3%
230 313
 
3.1%
720 305
 
3.0%
200 303
 
3.0%
150 268
 
2.7%
130 267
 
2.7%
730 252
 
2.5%
Other values (96) 6778
67.8%
ValueCountFrequency (%)
110 441
4.4%
111 160
 
1.6%
112 37
 
0.4%
113 165
 
1.7%
114 40
 
0.4%
115 51
 
0.5%
117 43
 
0.4%
121 42
 
0.4%
123 41
 
0.4%
125 45
 
0.4%
ValueCountFrequency (%)
940 30
 
0.3%
930 39
0.4%
920 32
 
0.3%
910 31
 
0.3%
900 85
0.9%
890 70
0.7%
880 81
0.8%
870 76
0.8%
860 72
0.7%
850 65
0.7%

직역코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
K
3310 
E
3269 
J
1718 
G
1703 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowG
3rd rowE
4th rowG
5th rowG

Common Values

ValueCountFrequency (%)
K 3310
33.1%
E 3269
32.7%
J 1718
17.2%
G 1703
17.0%

Length

2023-12-13T01:09:30.210717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:09:30.349741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
k 3310
33.1%
e 3269
32.7%
j 1718
17.2%
g 1703
17.0%

가입자구분코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8284
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:09:30.442621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5282786
Coefficient of variation (CV)0.52362658
Kurtosis-1.3693628
Mean4.8284
Median Absolute Deviation (MAD)2
Skewness-0.36400292
Sum48284
Variance6.3921927
MonotonicityNot monotonic
2023-12-13T01:09:30.547701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 1760
17.6%
6 1673
16.7%
1 1662
16.6%
7 1640
16.4%
2 1636
16.4%
8 1629
16.3%
ValueCountFrequency (%)
1 1662
16.6%
2 1636
16.4%
5 1760
17.6%
6 1673
16.7%
7 1640
16.4%
8 1629
16.3%
ValueCountFrequency (%)
8 1629
16.3%
7 1640
16.4%
6 1673
16.7%
5 1760
17.6%
2 1636
16.4%
1 1662
16.6%

일반대상자인원수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4635
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7046.3264
Minimum0
Maximum261379
Zeros1589
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:09:30.724535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median827.5
Q35907.5
95-th percentile33187.25
Maximum261379
Range261379
Interquartile range (IQR)5893.5

Descriptive statistics

Standard deviation16585.197
Coefficient of variation (CV)2.3537367
Kurtosis32.403447
Mean7046.3264
Median Absolute Deviation (MAD)827.5
Skewness4.7772118
Sum70463264
Variance2.7506876 × 108
MonotonicityNot monotonic
2023-12-13T01:09:30.883152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1589
 
15.9%
1 127
 
1.3%
2 123
 
1.2%
4 114
 
1.1%
3 108
 
1.1%
5 89
 
0.9%
6 71
 
0.7%
7 66
 
0.7%
9 41
 
0.4%
8 40
 
0.4%
Other values (4625) 7632
76.3%
ValueCountFrequency (%)
0 1589
15.9%
1 127
 
1.3%
2 123
 
1.2%
3 108
 
1.1%
4 114
 
1.1%
5 89
 
0.9%
6 71
 
0.7%
7 66
 
0.7%
8 40
 
0.4%
9 41
 
0.4%
ValueCountFrequency (%)
261379 1
< 0.1%
261288 1
< 0.1%
175976 1
< 0.1%
150236 1
< 0.1%
149641 1
< 0.1%
145049 1
< 0.1%
144916 1
< 0.1%
144677 1
< 0.1%
144490 1
< 0.1%
141869 1
< 0.1%

일반수검자인원수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3745
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2641.7439
Minimum0
Maximum114855
Zeros1608
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:09:31.028811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median254.5
Q32169.75
95-th percentile11976.9
Maximum114855
Range114855
Interquartile range (IQR)2158.75

Descriptive statistics

Standard deviation6369.6211
Coefficient of variation (CV)2.4111425
Kurtosis38.560112
Mean2641.7439
Median Absolute Deviation (MAD)254.5
Skewness5.0278655
Sum26417439
Variance40572073
MonotonicityNot monotonic
2023-12-13T01:09:31.183260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1608
 
16.1%
1 141
 
1.4%
2 134
 
1.3%
4 122
 
1.2%
5 94
 
0.9%
3 93
 
0.9%
7 76
 
0.8%
6 72
 
0.7%
8 58
 
0.6%
14 51
 
0.5%
Other values (3735) 7551
75.5%
ValueCountFrequency (%)
0 1608
16.1%
1 141
 
1.4%
2 134
 
1.3%
3 93
 
0.9%
4 122
 
1.2%
5 94
 
0.9%
6 72
 
0.7%
7 76
 
0.8%
8 58
 
0.6%
9 50
 
0.5%
ValueCountFrequency (%)
114855 1
< 0.1%
110273 1
< 0.1%
65527 1
< 0.1%
63177 1
< 0.1%
61761 1
< 0.1%
60666 1
< 0.1%
59658 1
< 0.1%
54853 1
< 0.1%
54561 1
< 0.1%
53910 1
< 0.1%

구강수검자인원수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2370
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean957.2243
Minimum0
Maximum61970
Zeros1965
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:09:31.325314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median64
Q3629.25
95-th percentile4338.3
Maximum61970
Range61970
Interquartile range (IQR)627.25

Descriptive statistics

Standard deviation2852.5741
Coefficient of variation (CV)2.9800477
Kurtosis70.904543
Mean957.2243
Median Absolute Deviation (MAD)64
Skewness6.7242808
Sum9572243
Variance8137178.8
MonotonicityNot monotonic
2023-12-13T01:09:31.485658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1965
 
19.7%
1 395
 
4.0%
2 253
 
2.5%
3 149
 
1.5%
5 120
 
1.2%
4 108
 
1.1%
6 100
 
1.0%
7 87
 
0.9%
13 76
 
0.8%
11 74
 
0.7%
Other values (2360) 6673
66.7%
ValueCountFrequency (%)
0 1965
19.7%
1 395
 
4.0%
2 253
 
2.5%
3 149
 
1.5%
4 108
 
1.1%
5 120
 
1.2%
6 100
 
1.0%
7 87
 
0.9%
8 62
 
0.6%
9 56
 
0.6%
ValueCountFrequency (%)
61970 1
< 0.1%
59720 1
< 0.1%
35626 1
< 0.1%
33717 1
< 0.1%
33165 1
< 0.1%
32842 1
< 0.1%
29341 1
< 0.1%
28922 1
< 0.1%
28715 1
< 0.1%
28489 1
< 0.1%

Interactions

2023-12-13T01:09:28.132496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:24.972474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.568469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:26.123806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:26.975535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.518863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:28.244048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.078204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.667199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:26.226152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.069755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.623498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:28.330654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.161837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.762423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:26.336631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.147545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.709046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:28.430467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.265611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.859147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:26.427010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.263198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.824252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:28.529224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.358984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.950242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:26.516443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.342100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.948452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:28.627994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:25.470027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:26.034228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:26.606877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:27.423682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:28.036999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:09:31.585794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자통계유형구분코드시도코드시군구코드직역코드가입자구분코드일반대상자인원수일반수검자인원수구강수검자인원수
기준일자1.0000.0000.0160.0000.0000.0060.0000.0210.000
통계유형구분코드0.0001.0000.0450.0070.8290.8190.1530.0940.075
시도코드0.0160.0451.0000.5210.0460.0200.2280.1170.120
시군구코드0.0000.0070.5211.0000.0240.0000.1990.1590.143
직역코드0.0000.8290.0460.0241.0000.8460.3950.2410.225
가입자구분코드0.0060.8190.0200.0000.8461.0000.3150.2870.297
일반대상자인원수0.0000.1530.2280.1990.3950.3151.0000.8890.795
일반수검자인원수0.0210.0940.1170.1590.2410.2870.8891.0000.964
구강수검자인원수0.0000.0750.1200.1430.2250.2970.7950.9641.000
2023-12-13T01:09:31.709165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계유형구분코드직역코드기준일자
통계유형구분코드1.0000.6230.000
직역코드0.6231.0000.000
기준일자0.0000.0001.000
2023-12-13T01:09:31.818229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시군구코드가입자구분코드일반대상자인원수일반수검자인원수구강수검자인원수기준일자통계유형구분코드직역코드
시도코드1.0000.2270.015-0.147-0.143-0.1850.0000.0350.022
시군구코드0.2271.0000.017-0.164-0.164-0.2030.0000.0060.014
가입자구분코드0.0150.0171.000-0.372-0.427-0.4160.0030.6230.709
일반대상자인원수-0.147-0.164-0.3721.0000.9940.9790.0000.1150.185
일반수검자인원수-0.143-0.164-0.4270.9941.0000.9850.0090.1000.168
구강수검자인원수-0.185-0.203-0.4160.9790.9851.0000.0000.0800.156
기준일자0.0000.0000.0030.0000.0090.0001.0000.0000.000
통계유형구분코드0.0350.0060.6230.1150.1000.0800.0001.0000.623
직역코드0.0220.0140.7090.1850.1680.1560.0000.6231.000

Missing values

2023-12-13T01:09:28.747869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:09:28.934730image/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

기준일자사업년도통계유형구분코드시도코드시군구코드직역코드가입자구분코드일반대상자인원수일반수검자인원수구강수검자인원수
898232023-08-2520232147840E7000
563522023-08-1520231129140G639671549677
262642023-08-0520231144800E752815228
193142023-08-0320231141115G532681247490
46442023-07-2920231144825G5931340212
192012023-08-0320231130140K61675463712580
324252023-08-0720231144710K647831953353
672532023-08-1820231148123J283952818843
721172023-08-2020231141171K112512525
683442023-08-1920231111560J21517152481591
기준일자사업년도통계유형구분코드시도코드시군구코드직역코드가입자구분코드일반대상자인원수일반수검자인원수구강수검자인원수
418112023-08-1020231145113K111111162
336442023-08-0720232130140E7000
688362023-08-1920231130170G661082292989
519072023-08-1320231148870K544781735304
448662023-08-1120231144800E752716328
85202023-07-3020231148850K642631607159
13042023-07-2820231143130K132323
158222023-08-0220231126500J11282043141311
72362023-07-3020231141463K2989820
203242023-08-0320231146730E7197402