Overview

Dataset statistics

Number of variables13
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory118.4 B

Variable types

Categorical4
Numeric9

Dataset

Description국민연금공단 장애유형별 심사결과 현황(연간)입니다.(등급결정 : 1급,2급,3급,4급,초진일결정 / 미해당 : 등급외, 결정보류, 자격미달, 확인불가)
URLhttps://www.data.go.kr/data/15041658/fileData.do

Alerts

합계 is highly overall correlated with 1급 and 9 other fieldsHigh correlation
1급 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
2급 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
3급 is highly overall correlated with 합계 and 7 other fieldsHigh correlation
4급 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
등급외 is highly overall correlated with 합계 and 9 other fieldsHigh correlation
결정보류 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
자격미달 is highly overall correlated with 합계 and 7 other fieldsHigh correlation
확인불가 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
구분명 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
초진일결정 is highly overall correlated with 합계 and 6 other fieldsHigh correlation
초진일결정 is highly imbalanced (52.9%)Imbalance
인과관계 is highly imbalanced (73.9%)Imbalance
합계 has unique valuesUnique
1급 has 16 (28.6%) zerosZeros
2급 has 8 (14.3%) zerosZeros
3급 has 2 (3.6%) zerosZeros
4급 has 2 (3.6%) zerosZeros
등급외 has 2 (3.6%) zerosZeros
결정보류 has 11 (19.6%) zerosZeros
자격미달 has 5 (8.9%) zerosZeros
확인불가 has 37 (66.1%) zerosZeros

Reproduction

Analysis started2023-12-12 09:30:17.211365
Analysis finished2023-12-12 09:30:26.706840
Duration9.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

Distinct3
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size580.0 B
2021
19 
2022
19 
2020
18 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2021 19
33.9%
2022 19
33.9%
2020 18
32.1%

Length

2023-12-12T18:30:26.773018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:26.875498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 19
33.9%
2022 19
33.9%
2020 18
32.1%

구분명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size580.0 B
눈의 장애
 
3
귀의 장애
 
3
입의 장애
 
3
지체의 장애(팔)
 
3
지체의 장애(다리)
 
3
Other values (14)
41 

Length

Max length19
Median length12
Mean length9.2321429
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row눈의 장애
2nd row귀의 장애
3rd row입의 장애
4th row지체의 장애(팔)
5th row지체의 장애(다리)

Common Values

ValueCountFrequency (%)
눈의 장애 3
 
5.4%
귀의 장애 3
 
5.4%
입의 장애 3
 
5.4%
지체의 장애(팔) 3
 
5.4%
지체의 장애(다리) 3
 
5.4%
지체의 장애(척추) 3
 
5.4%
지체의 장애(사지마비) 3
 
5.4%
정신 또는 신경계통의 장애 3
 
5.4%
호흡기의 장애 3
 
5.4%
심장의 장애 3
 
5.4%
Other values (9) 26
46.4%

Length

2023-12-12T18:30:27.021901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
장애 33
26.4%
지체의 18
14.4%
사지마비 6
 
4.8%
등 6
 
4.8%
눈의 3
 
2.4%
신장의 3
 
2.4%
장애(척수손상 3
 
2.4%
장애(뇌손상 3
 
2.4%
악성신생물(고형암)의 3
 
2.4%
안면의 3
 
2.4%
Other values (15) 44
35.2%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1621.375
Minimum1
Maximum10781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T18:30:27.195451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.75
Q1207.5
median612
Q31533.25
95-th percentile8187.75
Maximum10781
Range10780
Interquartile range (IQR)1325.75

Descriptive statistics

Standard deviation2676.5696
Coefficient of variation (CV)1.6508023
Kurtosis5.0189968
Mean1621.375
Median Absolute Deviation (MAD)514.5
Skewness2.4265302
Sum90797
Variance7164024.7
MonotonicityNot monotonic
2023-12-12T18:30:27.379315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1319 1
 
1.8%
810 1
 
1.8%
257 1
 
1.8%
17 1
 
1.8%
10781 1
 
1.8%
3077 1
 
1.8%
460 1
 
1.8%
1 1
 
1.8%
1219 1
 
1.8%
484 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
1 1
1.8%
3 1
1.8%
17 1
1.8%
22 1
1.8%
32 1
1.8%
56 1
1.8%
66 1
1.8%
73 1
1.8%
86 1
1.8%
90 1
1.8%
ValueCountFrequency (%)
10781 1
1.8%
10428 1
1.8%
9759 1
1.8%
7664 1
1.8%
7295 1
1.8%
6906 1
1.8%
3373 1
1.8%
3097 1
1.8%
3077 1
1.8%
1862 1
1.8%

1급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.01786
Minimum0
Maximum1315
Zeros16
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T18:30:27.540586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q387
95-th percentile804.75
Maximum1315
Range1315
Interquartile range (IQR)87

Descriptive statistics

Standard deviation289.93262
Coefficient of variation (CV)2.2826131
Kurtosis7.5764849
Mean127.01786
Median Absolute Deviation (MAD)5
Skewness2.8549954
Sum7113
Variance84060.927
MonotonicityNot monotonic
2023-12-12T18:30:27.716001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 16
28.6%
2 4
 
7.1%
1 3
 
5.4%
3 3
 
5.4%
5 2
 
3.6%
124 1
 
1.8%
93 1
 
1.8%
197 1
 
1.8%
145 1
 
1.8%
10 1
 
1.8%
Other values (23) 23
41.1%
ValueCountFrequency (%)
0 16
28.6%
1 3
 
5.4%
2 4
 
7.1%
3 3
 
5.4%
4 1
 
1.8%
5 2
 
3.6%
7 1
 
1.8%
10 1
 
1.8%
12 1
 
1.8%
14 1
 
1.8%
ValueCountFrequency (%)
1315 1
1.8%
1108 1
1.8%
975 1
1.8%
748 1
1.8%
681 1
1.8%
619 1
1.8%
224 1
1.8%
197 1
1.8%
175 1
1.8%
145 1
1.8%

2급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623.60714
Minimum0
Maximum5569
Zeros8
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T18:30:27.891753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.5
median51.5
Q399.5
95-th percentile5293.5
Maximum5569
Range5569
Interquartile range (IQR)85

Descriptive statistics

Standard deviation1617.4782
Coefficient of variation (CV)2.5937455
Kurtosis5.1107351
Mean623.60714
Median Absolute Deviation (MAD)39.5
Skewness2.6144268
Sum34922
Variance2616235.8
MonotonicityNot monotonic
2023-12-12T18:30:28.065328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 8
 
14.3%
6 2
 
3.6%
7 2
 
3.6%
16 2
 
3.6%
113 1
 
1.8%
53 1
 
1.8%
18 1
 
1.8%
5541 1
 
1.8%
504 1
 
1.8%
78 1
 
1.8%
Other values (36) 36
64.3%
ValueCountFrequency (%)
0 8
14.3%
6 2
 
3.6%
7 2
 
3.6%
11 1
 
1.8%
13 1
 
1.8%
15 1
 
1.8%
16 2
 
3.6%
18 1
 
1.8%
21 1
 
1.8%
23 1
 
1.8%
ValueCountFrequency (%)
5569 1
1.8%
5541 1
1.8%
5511 1
1.8%
5221 1
1.8%
4831 1
1.8%
4701 1
1.8%
504 1
1.8%
493 1
1.8%
468 1
1.8%
151 1
1.8%

3급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean329.78571
Minimum0
Maximum2379
Zeros2
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T18:30:28.224948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.5
Q135
median79.5
Q3217.75
95-th percentile1328.25
Maximum2379
Range2379
Interquartile range (IQR)182.75

Descriptive statistics

Standard deviation549.91896
Coefficient of variation (CV)1.6675039
Kurtosis5.3300888
Mean329.78571
Median Absolute Deviation (MAD)61
Skewness2.3502695
Sum18468
Variance302410.86
MonotonicityNot monotonic
2023-12-12T18:30:28.377268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 3
 
5.4%
85 2
 
3.6%
27 2
 
3.6%
0 2
 
3.6%
186 2
 
3.6%
29 1
 
1.8%
4 1
 
1.8%
2210 1
 
1.8%
770 1
 
1.8%
82 1
 
1.8%
Other values (40) 40
71.4%
ValueCountFrequency (%)
0 2
3.6%
4 1
1.8%
10 1
1.8%
14 1
1.8%
16 1
1.8%
17 1
1.8%
20 1
1.8%
21 1
1.8%
22 1
1.8%
25 1
1.8%
ValueCountFrequency (%)
2379 1
1.8%
2210 1
1.8%
1920 1
1.8%
1131 1
1.8%
1103 1
1.8%
1080 1
1.8%
982 1
1.8%
906 1
1.8%
863 1
1.8%
797 1
1.8%

4급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.98214
Minimum0
Maximum723
Zeros2
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T18:30:28.524879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.75
Q120
median193.5
Q3304.75
95-th percentile641.5
Maximum723
Range723
Interquartile range (IQR)284.75

Descriptive statistics

Standard deviation233.02653
Coefficient of variation (CV)1.0592975
Kurtosis-0.63873899
Mean219.98214
Median Absolute Deviation (MAD)171.5
Skewness0.86876556
Sum12319
Variance54301.363
MonotonicityNot monotonic
2023-12-12T18:30:28.673624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
19 2
 
3.6%
0 2
 
3.6%
20 2
 
3.6%
28 2
 
3.6%
5 2
 
3.6%
8 2
 
3.6%
604 2
 
3.6%
723 1
 
1.8%
213 1
 
1.8%
4 1
 
1.8%
Other values (39) 39
69.6%
ValueCountFrequency (%)
0 2
3.6%
3 1
1.8%
4 1
1.8%
5 2
3.6%
6 1
1.8%
8 2
3.6%
14 1
1.8%
17 1
1.8%
19 2
3.6%
20 2
3.6%
ValueCountFrequency (%)
723 1
1.8%
660 1
1.8%
649 1
1.8%
639 1
1.8%
638 1
1.8%
612 1
1.8%
604 2
3.6%
596 1
1.8%
586 1
1.8%
556 1
1.8%

초진일결정
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size580.0 B
0
46 
1
5 
3
 
3
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 46
82.1%
1 5
 
8.9%
3 3
 
5.4%
2 2
 
3.6%

Length

2023-12-12T18:30:28.861379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:28.977248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 46
82.1%
1 5
 
8.9%
3 3
 
5.4%
2 2
 
3.6%

등급외
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.03571
Minimum0
Maximum1764
Zeros2
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T18:30:29.096237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q129
median170
Q3298.75
95-th percentile1281.25
Maximum1764
Range1764
Interquartile range (IQR)269.75

Descriptive statistics

Standard deviation426.05778
Coefficient of variation (CV)1.4947523
Kurtosis4.8340329
Mean285.03571
Median Absolute Deviation (MAD)138.5
Skewness2.3446417
Sum15962
Variance181525.24
MonotonicityNot monotonic
2023-12-12T18:30:29.623785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9 2
 
3.6%
6 2
 
3.6%
325 2
 
3.6%
13 2
 
3.6%
202 2
 
3.6%
0 2
 
3.6%
19 2
 
3.6%
144 1
 
1.8%
26 1
 
1.8%
1764 1
 
1.8%
Other values (39) 39
69.6%
ValueCountFrequency (%)
0 2
3.6%
6 2
3.6%
8 1
1.8%
9 2
3.6%
13 2
3.6%
14 1
1.8%
18 1
1.8%
19 2
3.6%
26 1
1.8%
30 1
1.8%
ValueCountFrequency (%)
1764 1
1.8%
1693 1
1.8%
1498 1
1.8%
1209 1
1.8%
1185 1
1.8%
1175 1
1.8%
484 1
1.8%
471 1
1.8%
470 1
1.8%
376 1
1.8%

결정보류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.267857
Minimum0
Maximum81
Zeros11
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T18:30:29.854153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310.25
95-th percentile55.25
Maximum81
Range81
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation19.04396
Coefficient of variation (CV)1.6901137
Kurtosis5.655001
Mean11.267857
Median Absolute Deviation (MAD)3.5
Skewness2.4755727
Sum631
Variance362.6724
MonotonicityNot monotonic
2023-12-12T18:30:30.015209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 11
19.6%
1 6
10.7%
4 6
10.7%
2 4
 
7.1%
3 4
 
7.1%
5 4
 
7.1%
11 2
 
3.6%
7 2
 
3.6%
6 2
 
3.6%
23 1
 
1.8%
Other values (14) 14
25.0%
ValueCountFrequency (%)
0 11
19.6%
1 6
10.7%
2 4
 
7.1%
3 4
 
7.1%
4 6
10.7%
5 4
 
7.1%
6 2
 
3.6%
7 2
 
3.6%
8 1
 
1.8%
9 1
 
1.8%
ValueCountFrequency (%)
81 1
1.8%
78 1
1.8%
65 1
1.8%
52 1
1.8%
44 1
1.8%
43 1
1.8%
30 1
1.8%
27 1
1.8%
23 1
1.8%
17 1
1.8%

자격미달
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.75
Minimum0
Maximum104
Zeros5
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T18:30:30.230416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.75
median13.5
Q334
95-th percentile82.75
Maximum104
Range104
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation26.84247
Coefficient of variation (CV)1.1798888
Kurtosis1.7766382
Mean22.75
Median Absolute Deviation (MAD)11.5
Skewness1.5410879
Sum1274
Variance720.51818
MonotonicityNot monotonic
2023-12-12T18:30:30.445735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 5
 
8.9%
0 5
 
8.9%
1 4
 
7.1%
4 4
 
7.1%
11 3
 
5.4%
5 3
 
5.4%
14 2
 
3.6%
3 2
 
3.6%
34 2
 
3.6%
20 2
 
3.6%
Other values (23) 24
42.9%
ValueCountFrequency (%)
0 5
8.9%
1 4
7.1%
2 5
8.9%
3 2
 
3.6%
4 4
7.1%
5 3
5.4%
6 1
 
1.8%
11 3
5.4%
13 1
 
1.8%
14 2
 
3.6%
ValueCountFrequency (%)
104 1
1.8%
100 1
1.8%
85 1
1.8%
82 1
1.8%
76 1
1.8%
71 1
1.8%
56 1
1.8%
48 1
1.8%
46 1
1.8%
42 1
1.8%

확인불가
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92857143
Minimum0
Maximum14
Zeros37
Zeros (%)66.1%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T18:30:30.606467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4.25
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2308347
Coefficient of variation (CV)2.4024373
Kurtosis21.698254
Mean0.92857143
Median Absolute Deviation (MAD)0
Skewness4.2096766
Sum52
Variance4.9766234
MonotonicityNot monotonic
2023-12-12T18:30:30.758497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 37
66.1%
1 11
 
19.6%
4 2
 
3.6%
3 2
 
3.6%
5 1
 
1.8%
6 1
 
1.8%
14 1
 
1.8%
2 1
 
1.8%
ValueCountFrequency (%)
0 37
66.1%
1 11
 
19.6%
2 1
 
1.8%
3 2
 
3.6%
4 2
 
3.6%
5 1
 
1.8%
6 1
 
1.8%
14 1
 
1.8%
ValueCountFrequency (%)
14 1
 
1.8%
6 1
 
1.8%
5 1
 
1.8%
4 2
 
3.6%
3 2
 
3.6%
2 1
 
1.8%
1 11
 
19.6%
0 37
66.1%

인과관계
Categorical

IMBALANCE 

Distinct5
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size580.0 B
0
51 
1
 
2
17
 
1
11
 
1
6
 
1

Length

Max length2
Median length1
Mean length1.0357143
Min length1

Unique

Unique3 ?
Unique (%)5.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 51
91.1%
1 2
 
3.6%
17 1
 
1.8%
11 1
 
1.8%
6 1
 
1.8%

Length

2023-12-12T18:30:30.923868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:31.032371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51
91.1%
1 2
 
3.6%
17 1
 
1.8%
11 1
 
1.8%
6 1
 
1.8%

Interactions

2023-12-12T18:30:25.452100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:17.755303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.619524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.550032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:20.522233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:21.482615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:22.502627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:23.456648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.663658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:25.566674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:17.830818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.699469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.641990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:20.638471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:21.600109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:22.618769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:23.875982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.739706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:25.656924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:17.934819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.824517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.740567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:20.753779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:21.705843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:22.734532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:23.970876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.818233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:25.744593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.035600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.924692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.826736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:20.847907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:21.810847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:22.847639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.057357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.892667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:25.831162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.147879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.024211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.925116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:20.939501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:21.930717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:22.953973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.141639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.977064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:25.926613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.254726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.136751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:20.044177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:21.068001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:22.046449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:23.055620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.239020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:25.062961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:26.058519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.351877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.247507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:20.194844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:21.175871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:22.152365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:23.146415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.348257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:25.190253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:26.158829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.447342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.354233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:20.317136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:21.289084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:22.283823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:23.241520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.450838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:25.275616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:26.268340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:18.531109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:19.454349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:20.412927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:21.381512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:22.383324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:23.353323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:24.535742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:25.358354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:30:31.128949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연도구분명합계1급2급3급4급초진일결정등급외결정보류자격미달확인불가인과관계
기준연도1.0000.0000.0000.0000.3860.0000.0000.0000.0000.0000.2790.2520.000
구분명0.0001.0000.9040.7600.7130.8730.8750.7160.9400.8120.7880.4290.380
합계0.0000.9041.0000.9520.7740.8210.8040.7780.8570.8740.8200.3330.209
1급0.0000.7600.9521.0000.7290.8300.0000.7930.8460.9140.7510.0000.000
2급0.3860.7130.7740.7291.0000.9840.0000.5660.9850.7461.0000.0000.000
3급0.0000.8730.8210.8300.9841.0000.2110.7050.9640.8520.8160.3330.430
4급0.0000.8750.8040.0000.0000.2111.0000.4160.5860.4630.6980.5150.000
초진일결정0.0000.7160.7780.7930.5660.7050.4161.0000.7360.9050.7390.2770.128
등급외0.0000.9400.8570.8460.9850.9640.5860.7361.0000.8780.8270.5190.000
결정보류0.0000.8120.8740.9140.7460.8520.4630.9050.8781.0000.6800.4330.000
자격미달0.2790.7880.8200.7511.0000.8160.6980.7390.8270.6801.0000.7840.850
확인불가0.2520.4290.3330.0000.0000.3330.5150.2770.5190.4330.7841.0000.633
인과관계0.0000.3800.2090.0000.0000.4300.0000.1280.0000.0000.8500.6331.000
2023-12-12T18:30:31.319717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분명기준연도인과관계초진일결정
구분명1.0000.0000.1510.401
기준연도0.0001.0000.0000.000
인과관계0.1510.0001.0000.097
초진일결정0.4010.0000.0971.000
2023-12-12T18:30:31.468391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계1급2급3급4급등급외결정보류자격미달확인불가기준연도구분명초진일결정인과관계
합계1.0000.6560.8280.8350.6610.9090.7630.8020.5980.0000.6040.6470.124
1급0.6561.0000.7490.6420.3640.5280.4950.4280.2880.0000.4000.6670.000
2급0.8280.7491.0000.7280.4840.6880.4900.5660.4190.1380.4230.5680.000
3급0.8350.6420.7281.0000.3460.6250.5920.5870.4050.0000.5510.5250.303
4급0.6610.3640.4840.3461.0000.6690.5010.5720.5220.0000.5520.2860.000
등급외0.9090.5280.6880.6250.6691.0000.7410.7990.5300.0000.6780.5590.000
결정보류0.7630.4950.4900.5920.5010.7411.0000.6210.4210.0000.4460.5840.000
자격미달0.8020.4280.5660.5870.5720.7990.6211.0000.6360.1520.3880.5120.487
확인불가0.5980.2880.4190.4050.5220.5300.4210.6361.0000.0960.1670.1740.487
기준연도0.0000.0000.1380.0000.0000.0000.0000.1520.0961.0000.0000.0000.000
구분명0.6040.4000.4230.5510.5520.6780.4460.3880.1670.0001.0000.4010.151
초진일결정0.6470.6670.5680.5250.2860.5590.5840.5120.1740.0000.4011.0000.097
인과관계0.1240.0000.0000.3030.0000.0000.0000.4870.4870.0000.1510.0971.000

Missing values

2023-12-12T18:30:26.438882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:30:26.631226image/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

기준연도구분명합계1급2급3급4급초진일결정등급외결정보류자격미달확인불가인과관계
02020눈의 장애131912495857230222234250
12020귀의 장애55000372270204304840
22020입의 장애8602822190141200
32020지체의 장애(팔)707562213238017611200
42020지체의 장애(다리)634128914283022141100
52020지체의 장애(척추)109712146612037692660
62020지체의 장애(사지마비)733727190132200
72020정신 또는 신경계통의 장애1862119151110323101674561417
82020호흡기의 장애2222447551705502400
92020심장의 장애164223672004011100
기준연도구분명합계1급2급3급4급초진일결정등급외결정보류자격미달확인불가인과관계
462022심장의 장애1471663280450400
472022신장의 장애7295252212205963117517610
482022간의 장애747143372307029052510
492022혈액·조혈기의장애1776527011312030296121110
502022복부·골반장기의 장애265016199200195600
512022안면의 장애2200105060100
522022악성신생물(고형암)의 장애1042861955692379311693818210
532022지체의 장애(뇌손상 등 사지마비)30977484687975481471442000
542022지체의 장애(척수손상 등 사지마비)4351755785710422300
552022기타30000000201