Overview

Dataset statistics

Number of variables7
Number of observations1365
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.1 KiB
Average record size in memory60.1 B

Variable types

Categorical1
Text2
Numeric4

Dataset

Description2018년도 병역판정검사 결과에 따른 국방부령 조항별 신체등급 4급, 5급, 6급 판정 현황입니다. (2018.12.31.기준, 단위: 명)
Author병무청
URLhttps://www.data.go.kr/data/15053161/fileData.do

Alerts

4급 is highly overall correlated with 5급High correlation
5급 is highly overall correlated with 4급High correlation
계 is highly skewed (γ1 = 26.97089871)Skewed
4급 is highly skewed (γ1 = 27.39102442)Skewed
부령조항 has unique valuesUnique
4급 has 692 (50.7%) zerosZeros
5급 has 837 (61.3%) zerosZeros
6급 has 1201 (88.0%) zerosZeros

Reproduction

Analysis started2023-12-12 05:53:09.254497
Analysis finished2023-12-12 05:53:11.584567
Duration2.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부령조항과
Categorical

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
정형외과
303 
내과
280 
정신과
106 
안과
100 
일반외과
95 
Other values (9)
481 

Length

Max length5
Median length4
Mean length3.2893773
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정형외과 303
22.2%
내과 280
20.5%
정신과 106
 
7.8%
안과 100
 
7.3%
일반외과 95
 
7.0%
이비인후과 87
 
6.4%
흉부외과 82
 
6.0%
신경외과 81
 
5.9%
신경과 69
 
5.1%
피부과 58
 
4.2%
Other values (4) 104
 
7.6%

Length

2023-12-12T14:53:11.652971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정형외과 303
22.2%
내과 280
20.5%
정신과 106
 
7.8%
안과 100
 
7.3%
일반외과 95
 
7.0%
이비인후과 87
 
6.4%
흉부외과 82
 
6.0%
신경외과 81
 
5.9%
신경과 69
 
5.1%
피부과 58
 
4.2%
Other values (4) 104
 
7.6%

부령조항
Text

UNIQUE 

Distinct1365
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
2023-12-12T14:53:11.954446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length10.880586
Min length7

Characters and Unicode

Total characters14852
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1365 ?
Unique (%)100.0%

Sample

1st row757 019-나
2nd row851 019-가
3rd row851 033-다
4th row872 006-라-1)
5th row872 011-가-2)-가)
ValueCountFrequency (%)
950 565
20.7%
968 450
 
16.5%
907 276
 
10.1%
872 65
 
2.4%
851 8
 
0.3%
096-나 5
 
0.2%
019-나 5
 
0.2%
102-다 5
 
0.2%
098-라 5
 
0.2%
286-다-3 5
 
0.2%
Other values (638) 1341
49.1%
2023-12-12T14:53:12.502083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2195
14.8%
9 1593
10.7%
0 1545
10.4%
1365
9.2%
2 1013
 
6.8%
8 869
 
5.9%
) 866
 
5.8%
5 841
 
5.7%
1 796
 
5.4%
6 752
 
5.1%
Other values (13) 3017
20.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8893
59.9%
Dash Punctuation 2195
 
14.8%
Other Letter 1530
 
10.3%
Space Separator 1365
 
9.2%
Close Punctuation 866
 
5.8%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1593
17.9%
0 1545
17.4%
2 1013
11.4%
8 869
9.8%
5 841
9.5%
1 796
9.0%
6 752
8.5%
7 577
 
6.5%
3 558
 
6.3%
4 349
 
3.9%
Other Letter
ValueCountFrequency (%)
나 595
38.9%
가 403
26.3%
다 305
19.9%
라 148
 
9.7%
마 37
 
2.4%
의 19
 
1.2%
사 11
 
0.7%
바 10
 
0.7%
아 2
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2195
100.0%
Space Separator
ValueCountFrequency (%)
1365
100.0%
Close Punctuation
ValueCountFrequency (%)
) 866
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13322
89.7%
Hangul 1530
 
10.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2195
16.5%
9 1593
12.0%
0 1545
11.6%
1365
10.2%
2 1013
7.6%
8 869
 
6.5%
) 866
 
6.5%
5 841
 
6.3%
1 796
 
6.0%
6 752
 
5.6%
Other values (4) 1487
11.2%
Hangul
ValueCountFrequency (%)
나 595
38.9%
가 403
26.3%
다 305
19.9%
라 148
 
9.7%
마 37
 
2.4%
의 19
 
1.2%
사 11
 
0.7%
바 10
 
0.7%
아 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13322
89.7%
Hangul 1530
 
10.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2195
16.5%
9 1593
12.0%
0 1545
11.6%
1365
10.2%
2 1013
7.6%
8 869
 
6.5%
) 866
 
6.5%
5 841
 
6.3%
1 796
 
6.0%
6 752
 
5.6%
Other values (4) 1487
11.2%
Hangul
ValueCountFrequency (%)
나 595
38.9%
가 403
26.3%
다 305
19.9%
라 148
 
9.7%
마 37
 
2.4%
의 19
 
1.2%
사 11
 
0.7%
바 10
 
0.7%
아 2
 
0.1%

질병명
Text

Distinct264
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
2023-12-12T14:53:12.931012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length32
Mean length9.1025641
Min length2

Characters and Unicode

Total characters12425
Distinct characters316
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)3.0%

Sample

1st row당뇨병
2nd row당뇨병
3rd row사구체신염
4th row척추관절병증
5th row그레이브씨병
ValueCountFrequency (%)
및 152
 
4.8%
또는 134
 
4.2%
질환 74
 
2.3%
경우 65
 
2.1%
결손 57
 
1.8%
장애 53
 
1.7%
그 44
 
1.4%
종양 43
 
1.4%
한 39
 
1.2%
손가락 36
 
1.1%
Other values (443) 2464
78.0%
2023-12-12T14:53:13.487311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1796
 
14.5%
성 370
 
3.0%
장 358
 
2.9%
질 285
 
2.3%
환 276
 
2.2%
애 240
 
1.9%
증 223
 
1.8%
경 212
 
1.7%
신 175
 
1.4%
손 174
 
1.4%
Other values (306) 8316
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10279
82.7%
Space Separator 1796
 
14.5%
Other Punctuation 172
 
1.4%
Lowercase Letter 71
 
0.6%
Open Punctuation 37
 
0.3%
Close Punctuation 37
 
0.3%
Uppercase Letter 18
 
0.1%
Decimal Number 15
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 370
 
3.6%
장 358
 
3.5%
질 285
 
2.8%
환 276
 
2.7%
애 240
 
2.3%
증 223
 
2.2%
경 212
 
2.1%
신 175
 
1.7%
손 174
 
1.7%
는 166
 
1.6%
Other values (281) 7800
75.9%
Lowercase Letter
ValueCountFrequency (%)
m 23
32.4%
e 11
15.5%
a 7
 
9.9%
p 6
 
8.5%
y 6
 
8.5%
c 5
 
7.0%
i 3
 
4.2%
n 3
 
4.2%
r 3
 
4.2%
s 2
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
T 6
33.3%
C 3
16.7%
P 3
16.7%
D 2
 
11.1%
R 2
 
11.1%
L 1
 
5.6%
I 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
· 123
71.5%
, 49
 
28.5%
Decimal Number
ValueCountFrequency (%)
5 12
80.0%
2 3
 
20.0%
Space Separator
ValueCountFrequency (%)
1796
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10279
82.7%
Common 2057
 
16.6%
Latin 89
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 370
 
3.6%
장 358
 
3.5%
질 285
 
2.8%
환 276
 
2.7%
애 240
 
2.3%
증 223
 
2.2%
경 212
 
2.1%
신 175
 
1.7%
손 174
 
1.7%
는 166
 
1.6%
Other values (281) 7800
75.9%
Latin
ValueCountFrequency (%)
m 23
25.8%
e 11
12.4%
a 7
 
7.9%
p 6
 
6.7%
y 6
 
6.7%
T 6
 
6.7%
c 5
 
5.6%
C 3
 
3.4%
P 3
 
3.4%
i 3
 
3.4%
Other values (8) 16
18.0%
Common
ValueCountFrequency (%)
1796
87.3%
· 123
 
6.0%
, 49
 
2.4%
( 37
 
1.8%
) 37
 
1.8%
5 12
 
0.6%
2 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10279
82.7%
ASCII 2023
 
16.3%
None 123
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1796
88.8%
, 49
 
2.4%
( 37
 
1.8%
) 37
 
1.8%
m 23
 
1.1%
5 12
 
0.6%
e 11
 
0.5%
a 7
 
0.3%
p 6
 
0.3%
y 6
 
0.3%
Other values (14) 39
 
1.9%
Hangul
ValueCountFrequency (%)
성 370
 
3.6%
장 358
 
3.5%
질 285
 
2.8%
환 276
 
2.7%
애 240
 
2.3%
증 223
 
2.2%
경 212
 
2.1%
신 175
 
1.7%
손 174
 
1.7%
는 166
 
1.6%
Other values (281) 7800
75.9%
None
ValueCountFrequency (%)
· 123
100.0%

계
Real number (ℝ)

SKEWED 

Distinct141
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.868864
Minimum1
Maximum11851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2023-12-12T14:53:13.641914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310
95-th percentile90.8
Maximum11851
Range11850
Interquartile range (IQR)9

Descriptive statistics

Standard deviation367.82788
Coefficient of variation (CV)9.9766534
Kurtosis817.37417
Mean36.868864
Median Absolute Deviation (MAD)2
Skewness26.970899
Sum50326
Variance135297.35
MonotonicityNot monotonic
2023-12-12T14:53:13.835447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 397
29.1%
2 205
15.0%
3 129
 
9.5%
4 80
 
5.9%
5 58
 
4.2%
6 45
 
3.3%
7 38
 
2.8%
8 31
 
2.3%
9 24
 
1.8%
10 20
 
1.5%
Other values (131) 338
24.8%
ValueCountFrequency (%)
1 397
29.1%
2 205
15.0%
3 129
 
9.5%
4 80
 
5.9%
5 58
 
4.2%
6 45
 
3.3%
7 38
 
2.8%
8 31
 
2.3%
9 24
 
1.8%
10 20
 
1.5%
ValueCountFrequency (%)
11851 1
0.1%
5499 1
0.1%
1830 1
0.1%
1522 1
0.1%
1376 1
0.1%
1209 1
0.1%
1074 1
0.1%
986 1
0.1%
711 1
0.1%
613 1
0.1%

4급
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct114
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.442491
Minimum0
Maximum11851
Zeros692
Zeros (%)50.7%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2023-12-12T14:53:14.003527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile54.8
Maximum11851
Range11851
Interquartile range (IQR)3

Descriptive statistics

Standard deviation366.12544
Coefficient of variation (CV)12.435274
Kurtosis834.92525
Mean29.442491
Median Absolute Deviation (MAD)0
Skewness27.391024
Sum40189
Variance134047.84
MonotonicityNot monotonic
2023-12-12T14:53:14.184472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 692
50.7%
1 167
 
12.2%
2 98
 
7.2%
3 67
 
4.9%
4 38
 
2.8%
5 31
 
2.3%
6 18
 
1.3%
7 17
 
1.2%
8 16
 
1.2%
10 13
 
1.0%
Other values (104) 208
 
15.2%
ValueCountFrequency (%)
0 692
50.7%
1 167
 
12.2%
2 98
 
7.2%
3 67
 
4.9%
4 38
 
2.8%
5 31
 
2.3%
6 18
 
1.3%
7 17
 
1.2%
8 16
 
1.2%
9 11
 
0.8%
ValueCountFrequency (%)
11851 1
0.1%
5499 1
0.1%
1830 1
0.1%
1522 1
0.1%
1376 1
0.1%
1209 1
0.1%
1074 1
0.1%
711 1
0.1%
525 1
0.1%
522 1
0.1%

5급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6014652
Minimum0
Maximum986
Zeros837
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2023-12-12T14:53:14.372240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile21
Maximum986
Range986
Interquartile range (IQR)2

Descriptive statistics

Standard deviation40.863294
Coefficient of variation (CV)6.190034
Kurtosis302.80853
Mean6.6014652
Median Absolute Deviation (MAD)0
Skewness15.393451
Sum9011
Variance1669.8088
MonotonicityNot monotonic
2023-12-12T14:53:14.516122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 837
61.3%
1 170
 
12.5%
2 84
 
6.2%
3 45
 
3.3%
4 29
 
2.1%
5 23
 
1.7%
6 21
 
1.5%
7 15
 
1.1%
8 11
 
0.8%
9 10
 
0.7%
Other values (58) 120
 
8.8%
ValueCountFrequency (%)
0 837
61.3%
1 170
 
12.5%
2 84
 
6.2%
3 45
 
3.3%
4 29
 
2.1%
5 23
 
1.7%
6 21
 
1.5%
7 15
 
1.1%
8 11
 
0.8%
9 10
 
0.7%
ValueCountFrequency (%)
986 1
0.1%
613 1
0.1%
521 1
0.1%
323 1
0.1%
276 1
0.1%
275 1
0.1%
273 1
0.1%
250 1
0.1%
184 1
0.1%
153 1
0.1%

6급
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82490842
Minimum0
Maximum107
Zeros1201
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size12.1 KiB
2023-12-12T14:53:14.643841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum107
Range107
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.312876
Coefficient of variation (CV)6.4405647
Kurtosis214.76494
Mean0.82490842
Median Absolute Deviation (MAD)0
Skewness13.28273
Sum1126
Variance28.226652
MonotonicityNot monotonic
2023-12-12T14:53:14.779885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 1201
88.0%
1 60
 
4.4%
2 23
 
1.7%
3 17
 
1.2%
4 13
 
1.0%
7 6
 
0.4%
6 6
 
0.4%
17 4
 
0.3%
5 4
 
0.3%
8 4
 
0.3%
Other values (17) 27
 
2.0%
ValueCountFrequency (%)
0 1201
88.0%
1 60
 
4.4%
2 23
 
1.7%
3 17
 
1.2%
4 13
 
1.0%
5 4
 
0.3%
6 6
 
0.4%
7 6
 
0.4%
8 4
 
0.3%
9 3
 
0.2%
ValueCountFrequency (%)
107 1
0.1%
88 1
0.1%
77 1
0.1%
58 1
0.1%
41 1
0.1%
31 2
0.1%
30 1
0.1%
27 1
0.1%
24 1
0.1%
18 2
0.1%

Interactions

2023-12-12T14:53:10.945833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:09.658120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:10.023746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:10.441146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:11.054745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:09.746962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:10.120084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:10.553227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:11.170166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:09.845703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:10.215601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:10.680889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:11.295863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:09.935974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:10.326268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:53:10.787219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:53:14.872534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부령조항과계4급5급6급
부령조항과1.0000.4820.4820.0000.090
계0.4821.0001.0000.0000.000
4급0.4821.0001.0000.0000.000
5급0.0000.0000.0001.0000.000
6급0.0900.0000.0000.0001.000
2023-12-12T14:53:15.002566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계4급5급6급부령조항과
계1.0000.3740.099-0.0600.292
4급0.3741.000-0.701-0.3380.292
5급0.099-0.7011.000-0.2820.000
6급-0.060-0.338-0.2821.0000.039
부령조항과0.2920.2920.0000.0391.000

Missing values

2023-12-12T14:53:11.424559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:53:11.540052image/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

부령조항과부령조항질병명계4급5급6급
0내과757 019-나당뇨병1010
1내과851 019-가당뇨병1100
2내과851 033-다사구체신염1010
3내과872 006-라-1)척추관절병증1100
4내과872 011-가-2)-가)그레이브씨병6600
5내과872 019-가당뇨병555500
6내과872 019-나당뇨병6060
7내과872 019-다당뇨병1010
8내과872 023-다철결핍 및 2차성 빈혈1010
9내과872 028-가-2)자반증1010
부령조항과부령조항질병명계4급5급6급
1355치과968 408-다-2)부정교합1010
1356치과968 410-라치아의 저작기능 평가6600
1357신장체중872 999신장체중363600
1358신장체중907 999신장체중1522152200
1359신장체중950 995신장체중800800
1360신장체중950 996신장체중7007
1361신장체중950 999신장체중118511185100
1362신장체중968 995신장체중480480
1363신장체중968 996신장체중7007
1364신장체중968 999신장체중5499549900