Overview

Dataset statistics

Number of variables8
Number of observations6526
Missing cells13052
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory420.8 KiB
Average record size in memory66.0 B

Variable types

Numeric2
Categorical1
Text5

Dataset

Description2015년부터 2022년까지 병역판정검사 수검자 중 4급~6급 판정 받은 사람의 부령조항과, 부령조항, 질병명 별 현황
Author병무청
URLhttps://www.data.go.kr/data/15112027/fileData.do

Alerts

4급 has 3284 (50.3%) missing valuesMissing
5급 has 4033 (61.8%) missing valuesMissing
6급 has 5735 (87.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:15:08.577932
Analysis finished2023-12-12 13:15:10.094070
Duration1.52 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.3331
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-12-12T22:15:10.459126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2019
Q32021
95-th percentile2022
Maximum2022
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5503896
Coefficient of variation (CV)0.0012636118
Kurtosis-1.5749903
Mean2018.3331
Median Absolute Deviation (MAD)2
Skewness0.032150266
Sum13171642
Variance6.504487
MonotonicityDecreasing
2023-12-12T22:15:10.586851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2021 1320
20.2%
2015 1244
19.1%
2016 1039
15.9%
2017 873
13.4%
2022 745
11.4%
2019 693
10.6%
2020 612
9.4%
ValueCountFrequency (%)
2015 1244
19.1%
2016 1039
15.9%
2017 873
13.4%
2019 693
10.6%
2020 612
9.4%
2021 1320
20.2%
2022 745
11.4%
ValueCountFrequency (%)
2022 745
11.4%
2021 1320
20.2%
2020 612
9.4%
2019 693
10.6%
2017 873
13.4%
2016 1039
15.9%
2015 1244
19.1%

부령조항과
Categorical

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
정형외과
1479 
내과
1281 
안과
467 
정신건강의학과
452 
일반외과
441 
Other values (9)
2406 

Length

Max length7
Median length5
Mean length3.6219736
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정형외과 1479
22.7%
내과 1281
19.6%
안과 467
 
7.2%
정신건강의학과 452
 
6.9%
일반외과 441
 
6.8%
이비인후과 430
 
6.6%
흉부외과 409
 
6.3%
신경외과 383
 
5.9%
신경과 341
 
5.2%
피부과 307
 
4.7%
Other values (4) 536
 
8.2%

Length

2023-12-12T22:15:10.726750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정형외과 1479
22.7%
내과 1281
19.6%
안과 467
 
7.2%
정신건강의학과 452
 
6.9%
일반외과 441
 
6.8%
이비인후과 430
 
6.6%
흉부외과 409
 
6.3%
신경외과 383
 
5.9%
신경과 341
 
5.2%
피부과 307
 
4.7%
Other values (4) 536
 
8.2%

부령조항
Text

Distinct4047
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
2023-12-12T22:15:11.039787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length10.965982
Min length7

Characters and Unicode

Total characters71564
Distinct characters26
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2153 ?
Unique (%)33.0%

Sample

1st row1043006-라-2)
2nd row1043011-가-2)-가)
3rd row1043011-가-2)-나)-(2)
4th row1043019-가
5th row1043019-나
ValueCountFrequency (%)
968 1600
 
14.1%
872 1206
 
10.6%
851 805
 
7.1%
907 764
 
6.7%
757 405
 
3.6%
950 48
 
0.4%
999 23
 
0.2%
103-나 17
 
0.1%
203-나 16
 
0.1%
260-다-2 16
 
0.1%
Other values (2017) 6457
56.9%
2023-12-12T22:15:11.551144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 10686
14.9%
1 7328
10.2%
2 5845
 
8.2%
0 5813
 
8.1%
8 5250
 
7.3%
4831
 
6.8%
) 4342
 
6.1%
6 4067
 
5.7%
9 3898
 
5.4%
7 3895
 
5.4%
Other values (16) 15609
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44319
61.9%
Dash Punctuation 10686
 
14.9%
Other Letter 7359
 
10.3%
Space Separator 4831
 
6.8%
Close Punctuation 4342
 
6.1%
Open Punctuation 26
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 2857
38.8%
가 1968
26.7%
다 1534
20.8%
라 623
 
8.5%
마 200
 
2.7%
의 70
 
1.0%
바 67
 
0.9%
사 30
 
0.4%
자 5
 
0.1%
아 3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 7328
16.5%
2 5845
13.2%
0 5813
13.1%
8 5250
11.8%
6 4067
9.2%
9 3898
8.8%
7 3895
8.8%
3 3356
7.6%
5 2562
 
5.8%
4 2305
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 10686
100.0%
Space Separator
ValueCountFrequency (%)
4831
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4342
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64204
89.7%
Hangul 7359
 
10.3%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 10686
16.6%
1 7328
11.4%
2 5845
9.1%
0 5813
9.1%
8 5250
8.2%
4831
7.5%
) 4342
6.8%
6 4067
 
6.3%
9 3898
 
6.1%
7 3895
 
6.1%
Other values (4) 8249
12.8%
Hangul
ValueCountFrequency (%)
나 2857
38.8%
가 1968
26.7%
다 1534
20.8%
라 623
 
8.5%
마 200
 
2.7%
의 70
 
1.0%
바 67
 
0.9%
사 30
 
0.4%
자 5
 
0.1%
아 3
 
< 0.1%
Latin
ValueCountFrequency (%)
a 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64205
89.7%
Hangul 7359
 
10.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 10686
16.6%
1 7328
11.4%
2 5845
9.1%
0 5813
9.1%
8 5250
8.2%
4831
7.5%
) 4342
6.8%
6 4067
 
6.3%
9 3898
 
6.1%
7 3895
 
6.1%
Other values (5) 8250
12.8%
Hangul
ValueCountFrequency (%)
나 2857
38.8%
가 1968
26.7%
다 1534
20.8%
라 623
 
8.5%
마 200
 
2.7%
의 70
 
1.0%
바 67
 
0.9%
사 30
 
0.4%
자 5
 
0.1%
아 3
 
< 0.1%

질병명
Text

Distinct373
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
2023-12-12T22:15:11.892785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length42
Mean length9.0318725
Min length2

Characters and Unicode

Total characters58942
Distinct characters349
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.5%

Sample

1st row척추관절병증
2nd row그레이브씨병
3rd row그레이브씨병
4th row당뇨병
5th row당뇨병
ValueCountFrequency (%)
및 694
 
4.6%
또는 673
 
4.5%
질환 337
 
2.3%
경우 310
 
2.1%
결손 286
 
1.9%
장애 231
 
1.5%
종양 211
 
1.4%
한 191
 
1.3%
그 180
 
1.2%
하지의 178
 
1.2%
Other values (584) 11674
78.0%
2023-12-12T22:15:12.388587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8439
 
14.3%
성 1651
 
2.8%
장 1638
 
2.8%
질 1360
 
2.3%
환 1326
 
2.2%
증 1100
 
1.9%
애 1040
 
1.8%
경 996
 
1.7%
는 830
 
1.4%
손 819
 
1.4%
Other values (339) 39743
67.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49006
83.1%
Space Separator 8439
 
14.3%
Other Punctuation 751
 
1.3%
Lowercase Letter 298
 
0.5%
Close Punctuation 158
 
0.3%
Open Punctuation 158
 
0.3%
Uppercase Letter 75
 
0.1%
Decimal Number 53
 
0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 1651
 
3.4%
장 1638
 
3.3%
질 1360
 
2.8%
환 1326
 
2.7%
증 1100
 
2.2%
애 1040
 
2.1%
경 996
 
2.0%
는 830
 
1.7%
손 819
 
1.7%
신 808
 
1.6%
Other values (301) 37438
76.4%
Lowercase Letter
ValueCountFrequency (%)
m 90
30.2%
e 40
13.4%
a 28
 
9.4%
c 24
 
8.1%
r 20
 
6.7%
y 20
 
6.7%
p 16
 
5.4%
s 12
 
4.0%
o 12
 
4.0%
n 12
 
4.0%
Other values (5) 24
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
T 16
21.3%
D 12
16.0%
C 8
10.7%
P 8
10.7%
R 8
10.7%
I 7
9.3%
L 6
 
8.0%
N 4
 
5.3%
S 2
 
2.7%
H 1
 
1.3%
Other values (3) 3
 
4.0%
Other Punctuation
ValueCountFrequency (%)
· 434
57.8%
, 216
28.8%
. 99
 
13.2%
" 2
 
0.3%
Decimal Number
ValueCountFrequency (%)
5 48
90.6%
2 5
 
9.4%
Space Separator
ValueCountFrequency (%)
8439
100.0%
Close Punctuation
ValueCountFrequency (%)
) 158
100.0%
Open Punctuation
ValueCountFrequency (%)
( 158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49006
83.1%
Common 9563
 
16.2%
Latin 373
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 1651
 
3.4%
장 1638
 
3.3%
질 1360
 
2.8%
환 1326
 
2.7%
증 1100
 
2.2%
애 1040
 
2.1%
경 996
 
2.0%
는 830
 
1.7%
손 819
 
1.7%
신 808
 
1.6%
Other values (301) 37438
76.4%
Latin
ValueCountFrequency (%)
m 90
24.1%
e 40
10.7%
a 28
 
7.5%
c 24
 
6.4%
r 20
 
5.4%
y 20
 
5.4%
p 16
 
4.3%
T 16
 
4.3%
D 12
 
3.2%
s 12
 
3.2%
Other values (18) 95
25.5%
Common
ValueCountFrequency (%)
8439
88.2%
· 434
 
4.5%
, 216
 
2.3%
) 158
 
1.7%
( 158
 
1.7%
. 99
 
1.0%
5 48
 
0.5%
2 5
 
0.1%
- 4
 
< 0.1%
" 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48923
83.0%
ASCII 9502
 
16.1%
None 434
 
0.7%
Compat Jamo 83
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8439
88.8%
, 216
 
2.3%
) 158
 
1.7%
( 158
 
1.7%
. 99
 
1.0%
m 90
 
0.9%
5 48
 
0.5%
e 40
 
0.4%
a 28
 
0.3%
c 24
 
0.3%
Other values (27) 202
 
2.1%
Hangul
ValueCountFrequency (%)
성 1651
 
3.4%
장 1638
 
3.3%
질 1360
 
2.8%
환 1326
 
2.7%
증 1100
 
2.2%
애 1040
 
2.1%
경 996
 
2.0%
는 830
 
1.7%
손 819
 
1.7%
신 808
 
1.7%
Other values (300) 37355
76.4%
None
ValueCountFrequency (%)
· 434
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 83
100.0%

계
Text

Distinct351
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
2023-12-12T22:15:12.774735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.376494
Min length1

Characters and Unicode

Total characters8983
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

Unique181 ?
Unique (%)2.8%

Sample

1st row1
2nd row13
3rd row1
4th row141
5th row4
ValueCountFrequency (%)
1 1797
27.5%
2 824
12.6%
3 540
 
8.3%
4 413
 
6.3%
5 274
 
4.2%
6 229
 
3.5%
7 175
 
2.7%
8 162
 
2.5%
9 133
 
2.0%
10 104
 
1.6%
Other values (341) 1875
28.7%
2023-12-12T22:15:13.297294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3001
33.4%
2 1531
17.0%
3 1019
 
11.3%
4 791
 
8.8%
5 580
 
6.5%
6 484
 
5.4%
7 452
 
5.0%
8 402
 
4.5%
9 355
 
4.0%
0 322
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8937
99.5%
Other Punctuation 46
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3001
33.6%
2 1531
17.1%
3 1019
 
11.4%
4 791
 
8.9%
5 580
 
6.5%
6 484
 
5.4%
7 452
 
5.1%
8 402
 
4.5%
9 355
 
4.0%
0 322
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3001
33.4%
2 1531
17.0%
3 1019
 
11.3%
4 791
 
8.8%
5 580
 
6.5%
6 484
 
5.4%
7 452
 
5.0%
8 402
 
4.5%
9 355
 
4.0%
0 322
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3001
33.4%
2 1531
17.0%
3 1019
 
11.3%
4 791
 
8.8%
5 580
 
6.5%
6 484
 
5.4%
7 452
 
5.0%
8 402
 
4.5%
9 355
 
4.0%
0 322
 
3.6%

4급
Text

MISSING 

Distinct301
Distinct (%)9.3%
Missing3284
Missing (%)50.3%
Memory size51.1 KiB
2023-12-12T22:15:13.689138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.4494139
Min length1

Characters and Unicode

Total characters4699
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

Unique173 ?
Unique (%)5.3%

Sample

1st row1
2nd row13
3rd row1
4th row141
5th row2
ValueCountFrequency (%)
1 841
25.9%
2 363
 
11.2%
3 256
 
7.9%
4 202
 
6.2%
5 131
 
4.0%
6 118
 
3.6%
7 90
 
2.8%
8 78
 
2.4%
9 62
 
1.9%
10 55
 
1.7%
Other values (291) 1046
32.3%
2023-12-12T22:15:14.324279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1484
31.6%
2 765
16.3%
3 546
 
11.6%
4 412
 
8.8%
5 319
 
6.8%
6 268
 
5.7%
7 254
 
5.4%
8 225
 
4.8%
9 196
 
4.2%
0 190
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4659
99.1%
Other Punctuation 40
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1484
31.9%
2 765
16.4%
3 546
 
11.7%
4 412
 
8.8%
5 319
 
6.8%
6 268
 
5.8%
7 254
 
5.5%
8 225
 
4.8%
9 196
 
4.2%
0 190
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1484
31.6%
2 765
16.3%
3 546
 
11.6%
4 412
 
8.8%
5 319
 
6.8%
6 268
 
5.7%
7 254
 
5.4%
8 225
 
4.8%
9 196
 
4.2%
0 190
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1484
31.6%
2 765
16.3%
3 546
 
11.6%
4 412
 
8.8%
5 319
 
6.8%
6 268
 
5.7%
7 254
 
5.4%
8 225
 
4.8%
9 196
 
4.2%
0 190
 
4.0%

5급
Text

MISSING 

Distinct175
Distinct (%)7.0%
Missing4033
Missing (%)61.8%
Memory size51.1 KiB
2023-12-12T22:15:14.700067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.3313277
Min length1

Characters and Unicode

Total characters3319
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

Unique83 ?
Unique (%)3.3%

Sample

1st row4
2nd row1
3rd row1
4th row5
5th row3
ValueCountFrequency (%)
1 702
28.2%
2 323
13.0%
3 212
 
8.5%
4 164
 
6.6%
5 110
 
4.4%
6 81
 
3.2%
7 72
 
2.9%
8 67
 
2.7%
9 52
 
2.1%
10 43
 
1.7%
Other values (165) 667
26.8%
2023-12-12T22:15:15.266700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1163
35.0%
2 569
17.1%
3 355
 
10.7%
4 299
 
9.0%
5 203
 
6.1%
6 169
 
5.1%
7 164
 
4.9%
8 147
 
4.4%
9 129
 
3.9%
0 115
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3313
99.8%
Other Punctuation 6
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1163
35.1%
2 569
17.2%
3 355
 
10.7%
4 299
 
9.0%
5 203
 
6.1%
6 169
 
5.1%
7 164
 
5.0%
8 147
 
4.4%
9 129
 
3.9%
0 115
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3319
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1163
35.0%
2 569
17.1%
3 355
 
10.7%
4 299
 
9.0%
5 203
 
6.1%
6 169
 
5.1%
7 164
 
4.9%
8 147
 
4.4%
9 129
 
3.9%
0 115
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1163
35.0%
2 569
17.1%
3 355
 
10.7%
4 299
 
9.0%
5 203
 
6.1%
6 169
 
5.1%
7 164
 
4.9%
8 147
 
4.4%
9 129
 
3.9%
0 115
 
3.5%

6급
Real number (ℝ)

MISSING 

Distinct64
Distinct (%)8.1%
Missing5735
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean8.4614412
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-12-12T22:15:15.461160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile38
Maximum134
Range133
Interquartile range (IQR)7

Descriptive statistics

Standard deviation15.737586
Coefficient of variation (CV)1.8599179
Kurtosis21.806343
Mean8.4614412
Median Absolute Deviation (MAD)2
Skewness4.1284642
Sum6693
Variance247.67161
MonotonicityNot monotonic
2023-12-12T22:15:15.627450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 254
 
3.9%
2 138
 
2.1%
3 72
 
1.1%
4 47
 
0.7%
5 33
 
0.5%
6 30
 
0.5%
9 19
 
0.3%
8 17
 
0.3%
12 14
 
0.2%
7 13
 
0.2%
Other values (54) 154
 
2.4%
(Missing) 5735
87.9%
ValueCountFrequency (%)
1 254
3.9%
2 138
2.1%
3 72
 
1.1%
4 47
 
0.7%
5 33
 
0.5%
6 30
 
0.5%
7 13
 
0.2%
8 17
 
0.3%
9 19
 
0.3%
10 6
 
0.1%
ValueCountFrequency (%)
134 1
< 0.1%
126 1
< 0.1%
125 1
< 0.1%
114 2
< 0.1%
102 1
< 0.1%
87 1
< 0.1%
78 1
< 0.1%
77 2
< 0.1%
73 1
< 0.1%
71 1
< 0.1%

Interactions

2023-12-12T22:15:09.477405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:15:09.164275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:15:09.616824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:15:09.334504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:15:15.746723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도부령조항과6급
연도1.0000.0000.179
부령조항과0.0001.0000.197
6급0.1790.1971.000
2023-12-12T22:15:15.848876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도6급부령조항과
연도1.0000.0190.000
6급0.0191.0000.080
부령조항과0.0000.0801.000

Missing values

2023-12-12T22:15:09.784121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:15:09.933543image/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.
2023-12-12T22:15:10.040608image/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

연도부령조항과부령조항질병명계4급5급6급
02022내과1043006-라-2)척추관절병증11<NA><NA>
12022내과1043011-가-2)-가)그레이브씨병1313<NA><NA>
22022내과1043011-가-2)-나)-(2)그레이브씨병11<NA><NA>
32022내과1043019-가당뇨병141141<NA><NA>
42022내과1043019-나당뇨병4<NA>4<NA>
52022내과1043019-다당뇨병1<NA>1<NA>
62022내과1043022-가그 밖에 확인된 내분비·대사·유전·면역질환22<NA><NA>
72022내과1043024재생불량성 빈혈1<NA><NA>1
82022내과1043027-가혈액응고장애1<NA>1<NA>
92022내과1043028-가-2)자반증5<NA>5<NA>
연도부령조항과부령조항질병명계4급5급6급
65162015치과872 404-가-2)구강내종양 및 낭종11<NA><NA>
65172015치과872 408-다-1)부정교합66<NA><NA>
65182015치과872 410-라치아의 저작기능 평가33<NA><NA>
65192015신장체중757 999신장체중719719<NA><NA>
65202015신장체중851 995신장체중10<NA>10<NA>
65212015신장체중851 996신장체중10<NA><NA>10
65222015신장체중851 999신장체중5,2045,204<NA><NA>
65232015신장체중872 995신장체중1<NA>1<NA>
65242015신장체중872 996신장체중1<NA><NA>1
65252015신장체중872 999신장체중2,0352,035<NA><NA>