Overview

Dataset statistics

Number of variables13
Number of observations357
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.6 KiB
Average record size in memory116.4 B

Variable types

Categorical3
Text1
Numeric9

Dataset

Description국민연금공단 장애심사 상병별 심사결과 현황(연간)입니다.(등급결정 : 1급,2급,3급,4급,초진일결정 / 미해당 : 등급외, 결정보류, 자격미달, 확인불가)
URLhttps://www.data.go.kr/data/15041648/fileData.do

Alerts

합계 is highly overall correlated with 1급 and 6 other fieldsHigh correlation
1급 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
2급 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
3급 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
4급 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
등급외 is highly overall correlated with 합계 and 6 other fieldsHigh correlation
결정보류 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
자격미달 is highly overall correlated with 합계 and 2 other fieldsHigh correlation
초진일결정 is highly overall correlated with 자격미달High correlation
초진일결정 is highly imbalanced (86.0%)Imbalance
인과관계 is highly imbalanced (95.8%)Imbalance
1급 has 154 (43.1%) zerosZeros
2급 has 103 (28.9%) zerosZeros
3급 has 67 (18.8%) zerosZeros
4급 has 117 (32.8%) zerosZeros
등급외 has 43 (12.0%) zerosZeros
결정보류 has 211 (59.1%) zerosZeros
자격미달 has 153 (42.9%) zerosZeros
확인불가 has 328 (91.9%) zerosZeros

Reproduction

Analysis started2023-12-12 22:35:58.739777
Analysis finished2023-12-12 22:36:07.666409
Duration8.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2020
120 
2021
120 
2022
117 

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 (%)
2020 120
33.6%
2021 120
33.6%
2022 117
32.8%

Length

2023-12-13T07:36:07.759456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:36:07.869930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 120
33.6%
2021 120
33.6%
2022 117
32.8%
Distinct128
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-13T07:36:08.133319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length6.4537815
Min length2

Characters and Unicode

Total characters2304
Distinct characters199
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)2.2%

Sample

1st row해당없음
2nd row기타(총합장애)
3rd row결핵/결핵성파괴폐
4th row패혈증
5th row바이러스뇌막염
ValueCountFrequency (%)
손상 7
 
1.8%
손상등 6
 
1.6%
해당없음 3
 
0.8%
뇌출혈 3
 
0.8%
골절등 3
 
0.8%
악,안면골의 3
 
0.8%
기타(호흡기의장애 3
 
0.8%
간질성폐질환 3
 
0.8%
진폐증 3
 
0.8%
기관지확장증 3
 
0.8%
Other values (124) 343
90.3%
2023-12-13T07:36:08.562580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
88
 
3.8%
84
 
3.6%
( 80
 
3.5%
) 80
 
3.5%
76
 
3.3%
/ 73
 
3.2%
67
 
2.9%
61
 
2.6%
52
 
2.3%
47
 
2.0%
Other values (189) 1596
69.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2021
87.7%
Other Punctuation 91
 
3.9%
Open Punctuation 80
 
3.5%
Close Punctuation 80
 
3.5%
Space Separator 23
 
1.0%
Uppercase Letter 9
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
88
 
4.4%
84
 
4.2%
76
 
3.8%
67
 
3.3%
61
 
3.0%
52
 
2.6%
47
 
2.3%
47
 
2.3%
44
 
2.2%
44
 
2.2%
Other values (181) 1411
69.8%
Uppercase Letter
ValueCountFrequency (%)
S 3
33.3%
A 3
33.3%
L 3
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 73
80.2%
, 18
 
19.8%
Open Punctuation
ValueCountFrequency (%)
( 80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80
100.0%
Space Separator
ValueCountFrequency (%)
23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2021
87.7%
Common 274
 
11.9%
Latin 9
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
88
 
4.4%
84
 
4.2%
76
 
3.8%
67
 
3.3%
61
 
3.0%
52
 
2.6%
47
 
2.3%
47
 
2.3%
44
 
2.2%
44
 
2.2%
Other values (181) 1411
69.8%
Common
ValueCountFrequency (%)
( 80
29.2%
) 80
29.2%
/ 73
26.6%
23
 
8.4%
, 18
 
6.6%
Latin
ValueCountFrequency (%)
S 3
33.3%
A 3
33.3%
L 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2021
87.7%
ASCII 283
 
12.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
88
 
4.4%
84
 
4.2%
76
 
3.8%
67
 
3.3%
61
 
3.0%
52
 
2.6%
47
 
2.3%
47
 
2.3%
44
 
2.2%
44
 
2.2%
Other values (181) 1411
69.8%
ASCII
ValueCountFrequency (%)
( 80
28.3%
) 80
28.3%
/ 73
25.8%
23
 
8.1%
, 18
 
6.4%
S 3
 
1.1%
A 3
 
1.1%
L 3
 
1.1%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct186
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.33333
Minimum1
Maximum7603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-13T07:36:08.748816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q112
median47
Q3190
95-th percentile1289.8
Maximum7603
Range7602
Interquartile range (IQR)178

Descriptive statistics

Standard deviation747.78471
Coefficient of variation (CV)2.9401758
Kurtosis63.070069
Mean254.33333
Median Absolute Deviation (MAD)42
Skewness7.2524222
Sum90797
Variance559181.97
MonotonicityNot monotonic
2023-12-13T07:36:08.906690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22
 
6.2%
2 16
 
4.5%
3 8
 
2.2%
8 7
 
2.0%
4 6
 
1.7%
9 6
 
1.7%
12 6
 
1.7%
17 5
 
1.4%
34 5
 
1.4%
21 5
 
1.4%
Other values (176) 271
75.9%
ValueCountFrequency (%)
1 22
6.2%
2 16
4.5%
3 8
 
2.2%
4 6
 
1.7%
5 5
 
1.4%
6 4
 
1.1%
7 3
 
0.8%
8 7
 
2.0%
9 6
 
1.7%
10 4
 
1.1%
ValueCountFrequency (%)
7603 1
0.3%
7270 1
0.3%
6776 1
0.3%
2176 1
0.3%
2128 1
0.3%
1931 1
0.3%
1793 1
0.3%
1771 1
0.3%
1753 1
0.3%
1746 1
0.3%

1급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.92437
Minimum0
Maximum663
Zeros154
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-13T07:36:09.080683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.2
Maximum663
Range663
Interquartile range (IQR)9

Descriptive statistics

Standard deviation58.815722
Coefficient of variation (CV)2.951949
Kurtosis52.711418
Mean19.92437
Median Absolute Deviation (MAD)1
Skewness6.2584347
Sum7113
Variance3459.2892
MonotonicityNot monotonic
2023-12-13T07:36:09.227899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 154
43.1%
1 37
 
10.4%
4 19
 
5.3%
3 17
 
4.8%
2 16
 
4.5%
5 9
 
2.5%
10 7
 
2.0%
8 5
 
1.4%
9 4
 
1.1%
13 4
 
1.1%
Other values (61) 85
23.8%
ValueCountFrequency (%)
0 154
43.1%
1 37
 
10.4%
2 16
 
4.5%
3 17
 
4.8%
4 19
 
5.3%
5 9
 
2.5%
6 4
 
1.1%
7 3
 
0.8%
8 5
 
1.4%
9 4
 
1.1%
ValueCountFrequency (%)
663 1
 
0.3%
448 1
 
0.3%
399 1
 
0.3%
217 1
 
0.3%
198 3
0.8%
186 1
 
0.3%
176 1
 
0.3%
163 2
0.6%
160 1
 
0.3%
150 1
 
0.3%

2급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.820728
Minimum0
Maximum5473
Zeros103
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-13T07:36:09.383384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q324
95-th percentile334.8
Maximum5473
Range5473
Interquartile range (IQR)24

Descriptive statistics

Standard deviation490.59512
Coefficient of variation (CV)5.015247
Kurtosis91.606961
Mean97.820728
Median Absolute Deviation (MAD)5
Skewness9.1971373
Sum34922
Variance240683.57
MonotonicityNot monotonic
2023-12-13T07:36:09.530612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
28.9%
1 24
 
6.7%
2 17
 
4.8%
4 17
 
4.8%
3 14
 
3.9%
5 13
 
3.6%
6 12
 
3.4%
7 11
 
3.1%
11 8
 
2.2%
10 7
 
2.0%
Other values (88) 131
36.7%
ValueCountFrequency (%)
0 103
28.9%
1 24
 
6.7%
2 17
 
4.8%
3 14
 
3.9%
4 17
 
4.8%
5 13
 
3.6%
6 12
 
3.4%
7 11
 
3.1%
8 6
 
1.7%
9 3
 
0.8%
ValueCountFrequency (%)
5473 1
0.3%
5197 1
0.3%
4574 1
0.3%
1165 1
0.3%
1140 1
0.3%
1110 1
0.3%
1091 1
0.3%
1023 1
0.3%
956 1
0.3%
862 1
0.3%

3급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.731092
Minimum0
Maximum1100
Zeros67
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-13T07:36:09.672325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q329
95-th percentile292.8
Maximum1100
Range1100
Interquartile range (IQR)28

Descriptive statistics

Standard deviation137.28972
Coefficient of variation (CV)2.6539111
Kurtosis25.915761
Mean51.731092
Median Absolute Deviation (MAD)9
Skewness4.6935521
Sum18468
Variance18848.467
MonotonicityNot monotonic
2023-12-13T07:36:09.828528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
18.8%
1 28
 
7.8%
2 21
 
5.9%
3 15
 
4.2%
4 12
 
3.4%
11 11
 
3.1%
12 10
 
2.8%
9 10
 
2.8%
5 10
 
2.8%
14 9
 
2.5%
Other values (83) 164
45.9%
ValueCountFrequency (%)
0 67
18.8%
1 28
7.8%
2 21
 
5.9%
3 15
 
4.2%
4 12
 
3.4%
5 10
 
2.8%
6 8
 
2.2%
7 3
 
0.8%
8 7
 
2.0%
9 10
 
2.8%
ValueCountFrequency (%)
1100 1
0.3%
1045 1
0.3%
945 1
0.3%
807 1
0.3%
625 1
0.3%
569 1
0.3%
523 1
0.3%
515 1
0.3%
512 1
0.3%
466 1
0.3%

4급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.507003
Minimum0
Maximum651
Zeros117
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-13T07:36:10.007027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q327
95-th percentile172.2
Maximum651
Range651
Interquartile range (IQR)27

Descriptive statistics

Standard deviation76.771352
Coefficient of variation (CV)2.224805
Kurtosis29.707704
Mean34.507003
Median Absolute Deviation (MAD)4
Skewness4.6519223
Sum12319
Variance5893.8405
MonotonicityNot monotonic
2023-12-13T07:36:10.180880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117
32.8%
1 23
 
6.4%
3 16
 
4.5%
2 15
 
4.2%
11 13
 
3.6%
7 13
 
3.6%
8 10
 
2.8%
4 8
 
2.2%
5 5
 
1.4%
6 5
 
1.4%
Other values (92) 132
37.0%
ValueCountFrequency (%)
0 117
32.8%
1 23
 
6.4%
2 15
 
4.2%
3 16
 
4.5%
4 8
 
2.2%
5 5
 
1.4%
6 5
 
1.4%
7 13
 
3.6%
8 10
 
2.8%
9 3
 
0.8%
ValueCountFrequency (%)
651 1
0.3%
637 1
0.3%
596 1
0.3%
302 1
0.3%
287 1
0.3%
257 1
0.3%
215 1
0.3%
202 1
0.3%
194 2
0.6%
189 1
0.3%

초진일결정
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
0
342 
1
 
13
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 342
95.8%
1 13
 
3.6%
2 1
 
0.3%
3 1
 
0.3%

Length

2023-12-13T07:36:10.358255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:36:10.500417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 342
95.8%
1 13
 
3.6%
2 1
 
0.3%
3 1
 
0.3%

등급외
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.711485
Minimum0
Maximum1208
Zeros43
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-13T07:36:10.620008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q330
95-th percentile151.6
Maximum1208
Range1208
Interquartile range (IQR)28

Descriptive statistics

Standard deviation124.84002
Coefficient of variation (CV)2.7921243
Kurtosis58.321928
Mean44.711485
Median Absolute Deviation (MAD)9
Skewness6.9857068
Sum15962
Variance15585.032
MonotonicityNot monotonic
2023-12-13T07:36:10.792722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43
 
12.0%
2 27
 
7.6%
1 20
 
5.6%
3 19
 
5.3%
8 15
 
4.2%
5 12
 
3.4%
11 12
 
3.4%
12 12
 
3.4%
9 11
 
3.1%
10 10
 
2.8%
Other values (95) 176
49.3%
ValueCountFrequency (%)
0 43
12.0%
1 20
5.6%
2 27
7.6%
3 19
5.3%
4 8
 
2.2%
5 12
 
3.4%
6 7
 
2.0%
7 8
 
2.2%
8 15
 
4.2%
9 11
 
3.1%
ValueCountFrequency (%)
1208 1
0.3%
1174 1
0.3%
1173 1
0.3%
546 1
0.3%
431 1
0.3%
425 1
0.3%
341 1
0.3%
308 1
0.3%
302 1
0.3%
299 1
0.3%

결정보류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.767507
Minimum0
Maximum26
Zeros211
Zeros (%)59.1%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-13T07:36:10.933646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile9
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.8721503
Coefficient of variation (CV)2.1907411
Kurtosis16.374939
Mean1.767507
Median Absolute Deviation (MAD)0
Skewness3.7517601
Sum631
Variance14.993548
MonotonicityNot monotonic
2023-12-13T07:36:11.071661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 211
59.1%
1 47
 
13.2%
2 26
 
7.3%
4 18
 
5.0%
3 16
 
4.5%
5 8
 
2.2%
9 5
 
1.4%
8 4
 
1.1%
6 4
 
1.1%
7 4
 
1.1%
Other values (10) 14
 
3.9%
ValueCountFrequency (%)
0 211
59.1%
1 47
 
13.2%
2 26
 
7.3%
3 16
 
4.5%
4 18
 
5.0%
5 8
 
2.2%
6 4
 
1.1%
7 4
 
1.1%
8 4
 
1.1%
9 5
 
1.4%
ValueCountFrequency (%)
26 1
0.3%
24 1
0.3%
23 1
0.3%
22 2
0.6%
21 2
0.6%
16 1
0.3%
14 1
0.3%
12 1
0.3%
11 2
0.6%
10 2
0.6%

자격미달
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5686275
Minimum0
Maximum100
Zeros153
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-13T07:36:11.237866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile15
Maximum100
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.1174559
Coefficient of variation (CV)2.5548915
Kurtosis62.125786
Mean3.5686275
Median Absolute Deviation (MAD)1
Skewness7.0266073
Sum1274
Variance83.128002
MonotonicityNot monotonic
2023-12-13T07:36:11.402509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 153
42.9%
1 61
 
17.1%
2 34
 
9.5%
3 20
 
5.6%
5 18
 
5.0%
6 11
 
3.1%
4 10
 
2.8%
10 6
 
1.7%
7 5
 
1.4%
8 5
 
1.4%
Other values (19) 34
 
9.5%
ValueCountFrequency (%)
0 153
42.9%
1 61
 
17.1%
2 34
 
9.5%
3 20
 
5.6%
4 10
 
2.8%
5 18
 
5.0%
6 11
 
3.1%
7 5
 
1.4%
8 5
 
1.4%
9 4
 
1.1%
ValueCountFrequency (%)
100 1
0.3%
84 1
0.3%
76 1
0.3%
27 1
0.3%
26 2
0.6%
23 1
0.3%
22 1
0.3%
21 2
0.6%
20 2
0.6%
19 2
0.6%

확인불가
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14565826
Minimum0
Maximum11
Zeros328
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-13T07:36:11.506375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73137121
Coefficient of variation (CV)5.0211447
Kurtosis139.9045
Mean0.14565826
Median Absolute Deviation (MAD)0
Skewness10.3377
Sum52
Variance0.53490385
MonotonicityNot monotonic
2023-12-13T07:36:11.636752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 328
91.9%
1 19
 
5.3%
2 6
 
1.7%
3 2
 
0.6%
11 1
 
0.3%
4 1
 
0.3%
ValueCountFrequency (%)
0 328
91.9%
1 19
 
5.3%
2 6
 
1.7%
3 2
 
0.6%
4 1
 
0.3%
11 1
 
0.3%
ValueCountFrequency (%)
11 1
 
0.3%
4 1
 
0.3%
3 2
 
0.6%
2 6
 
1.7%
1 19
 
5.3%
0 328
91.9%

인과관계
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
0
354 
17
 
1
12
 
1
7
 
1

Length

Max length2
Median length1
Mean length1.0056022
Min length1

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row17
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 354
99.2%
17 1
 
0.3%
12 1
 
0.3%
7 1
 
0.3%

Length

2023-12-13T07:36:11.785175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:36:11.908735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 354
99.2%
17 1
 
0.3%
12 1
 
0.3%
7 1
 
0.3%

Interactions

2023-12-13T07:36:06.371221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:59.354344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:00.560010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.424011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.168344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.963274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.709399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.410365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.282550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:06.478666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:59.447758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:00.663023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.529199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.289747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.052993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.787966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.490381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.359716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:06.570462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:59.537931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:00.756484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.628801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.371469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.131135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.862429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.587889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.434747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:06.672332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:59.626653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:00.833249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.701747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.441565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.204993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.944231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.695634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.512722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:06.768758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:59.713900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:00.926315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.794171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.526750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.289722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.018452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.793022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.613089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:06.863989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:59.805747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.030521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.866462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.608907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.381420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.097519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.877158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.705304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:06.980914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:35:59.913742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.146088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.941797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.699393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.461181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.174289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.014044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.787445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:07.087826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:00.359880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.232636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.014630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.784900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.539090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.248523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.105520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.859149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:07.212471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:00.456471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:01.325446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.093085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:02.878734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:03.632401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:04.333478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:05.192658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:06.282127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:36:12.008203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연도합계1급2급3급4급초진일결정등급외결정보류자격미달확인불가인과관계
기준연도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.1720.001
합계0.0001.0000.4700.9790.7750.7050.5570.7430.6310.8470.9120.000
1급0.0000.4701.0000.3360.7410.8190.3780.5470.6920.0620.0000.000
2급0.0000.9790.3361.0000.6760.6310.5360.7670.5620.8190.8910.000
3급0.0000.7750.7410.6761.0000.7190.4900.7570.8310.5870.7670.000
4급0.0000.7050.8190.6310.7191.0000.5890.8810.6160.8570.5030.000
초진일결정0.0000.5570.3780.5360.4900.5891.0000.5740.4300.7590.1450.000
등급외0.0000.7430.5470.7670.7570.8810.5741.0000.6680.8770.4700.000
결정보류0.0000.6310.6920.5620.8310.6160.4300.6681.0000.5190.2990.000
자격미달0.0410.8470.0620.8190.5870.8570.7590.8770.5191.0000.7590.000
확인불가0.1720.9120.0000.8910.7670.5030.1450.4700.2990.7591.0000.000
인과관계0.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
2023-12-13T07:36:12.447886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인과관계기준연도초진일결정
인과관계1.0000.0000.000
기준연도0.0001.0000.000
초진일결정0.0000.0001.000
2023-12-13T07:36:12.540682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계1급2급3급4급등급외결정보류자격미달확인불가기준연도초진일결정인과관계
합계1.0000.6990.7860.8390.6110.9140.7560.6270.2180.0000.4830.000
1급0.6991.0000.8000.6790.2440.5630.5830.3370.0330.0000.2520.000
2급0.7860.8001.0000.7670.2330.6510.5970.3790.0480.0000.4630.000
3급0.8390.6790.7671.0000.3710.7560.6520.4730.1300.0000.3330.000
4급0.6110.2440.2330.3711.0000.5990.4340.4550.2720.0000.4200.000
등급외0.9140.5630.6510.7560.5991.0000.6810.6140.2250.0000.4070.000
결정보류0.7560.5830.5970.6520.4340.6811.0000.4940.1040.0000.2860.000
자격미달0.6270.3370.3790.4730.4550.6140.4941.0000.3060.0360.6000.000
확인불가0.2180.0330.0480.1300.2720.2250.1040.3061.0000.1300.1180.000
기준연도0.0000.0000.0000.0000.0000.0000.0000.0360.1301.0000.0000.000
초진일결정0.4830.2520.4630.3330.4200.4070.2860.6000.1180.0001.0000.000
인과관계0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T07:36:07.357752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:36:07.577498image/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
02020해당없음1700000000017
12020기타(총합장애)30012000000
22020결핵/결핵성파괴폐38110121050900
32020패혈증11000000000
42020바이러스뇌막염10010000000
52020설암264690050200
62020구강암50812195050100
72020후두암/인두암933502000191000
82020비인강암4272390030000
92020식도암622221110061100
기준연도구분명합계1급2급3급4급초진일결정등급외결정보류자격미달확인불가인과관계
3472022(다리)신경 손상3400026080000
3482022척추골절22529291490342000
3492022기타(척추의장애)1315212700362400
3502022(팔)절단,골절및 손상등50123416918001114100
3512022기타(팔의 장애)331598090100
3522022(다리)절단,골절및 손상등3248611412701104000
3532022기타(다리의장애)45083160170100
3542022화상50172470101000
3552022뇌손상/뇌좌상15999132811053000
3562022척수손상4021635267880282200