Overview

Dataset statistics

Number of variables8
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory73.4 B

Variable types

Categorical2
Numeric6

Dataset

Description2019,2020,2021년도 지방병무청별 병역판정검사의 결과 역종별 처분(현역, 보충역, 전시근로역, 병역면제, 재신체검사 대상)의 데이터입니다.
URLhttps://www.data.go.kr/data/15062500/fileData.do

Alerts

처분인원 is highly overall correlated with 현역 and 5 other fieldsHigh correlation
현역 is highly overall correlated with 처분인원 and 5 other fieldsHigh correlation
보충역 is highly overall correlated with 처분인원 and 4 other fieldsHigh correlation
전시근로역 is highly overall correlated with 처분인원 and 5 other fieldsHigh correlation
병역면제 is highly overall correlated with 처분인원 and 4 other fieldsHigh correlation
재신체검사 is highly overall correlated with 처분인원 and 4 other fieldsHigh correlation
지방청 is highly overall correlated with 처분인원 and 2 other fieldsHigh correlation
처분인원 has unique valuesUnique
현역 has unique valuesUnique
보충역 has unique valuesUnique
재신체검사 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:12:24.240807
Analysis finished2023-12-12 13:12:28.871230
Duration4.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct4
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size580.0 B
2019
14 
2020
14 
2021
14 
2022
14 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 14
25.0%
2020 14
25.0%
2021 14
25.0%
2022 14
25.0%

Length

2023-12-12T22:12:28.950842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:12:29.071822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 14
25.0%
2020 14
25.0%
2021 14
25.0%
2022 14
25.0%

지방청
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
서울
4 
부산.울산
4 
대구.경북
4 
경인
4 
광주.전남
4 
Other values (9)
36 

Length

Max length5
Median length2
Mean length3.1428571
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row부산.울산
3rd row대구.경북
4th row경인
5th row광주.전남

Common Values

ValueCountFrequency (%)
서울 4
 
7.1%
부산.울산 4
 
7.1%
대구.경북 4
 
7.1%
경인 4
 
7.1%
광주.전남 4
 
7.1%
대전.충남 4
 
7.1%
강원 4
 
7.1%
충북 4
 
7.1%
전북 4
 
7.1%
경남 4
 
7.1%
Other values (4) 16
28.6%

Length

2023-12-12T22:12:29.190482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 4
 
7.1%
부산.울산 4
 
7.1%
대구.경북 4
 
7.1%
경인 4
 
7.1%
광주.전남 4
 
7.1%
대전.충남 4
 
7.1%
강원 4
 
7.1%
충북 4
 
7.1%
전북 4
 
7.1%
경남 4
 
7.1%
Other values (4) 16
28.6%

처분인원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19797.357
Minimum2541
Maximum55189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T22:12:29.346828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2541
5-th percentile3367.25
Q18412.5
median19593.5
Q327172
95-th percentile42934.5
Maximum55189
Range52648
Interquartile range (IQR)18759.5

Descriptive statistics

Standard deviation13215.813
Coefficient of variation (CV)0.6675544
Kurtosis-0.17151998
Mean19797.357
Median Absolute Deviation (MAD)9670.5
Skewness0.64155107
Sum1108652
Variance1.7465771 × 108
MonotonicityNot monotonic
2023-12-12T22:12:29.490071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55189 1
 
1.8%
20096 1
 
1.8%
38253 1
 
1.8%
17202 1
 
1.8%
19773 1
 
1.8%
4778 1
 
1.8%
7700 1
 
1.8%
10182 1
 
1.8%
16591 1
 
1.8%
3621 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
2541 1
1.8%
2687 1
1.8%
3032 1
1.8%
3479 1
1.8%
3621 1
1.8%
3677 1
1.8%
3866 1
1.8%
4347 1
1.8%
4401 1
1.8%
4778 1
1.8%
ValueCountFrequency (%)
55189 1
1.8%
47289 1
1.8%
46041 1
1.8%
41899 1
1.8%
41703 1
1.8%
41462 1
1.8%
38253 1
1.8%
37514 1
1.8%
35736 1
1.8%
32240 1
1.8%

현역
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16270.536
Minimum2202
Maximum44341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T22:12:29.622164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2202
5-th percentile2796.75
Q16945.25
median15867.5
Q322038.25
95-th percentile35230.75
Maximum44341
Range42139
Interquartile range (IQR)15093

Descriptive statistics

Standard deviation10743.628
Coefficient of variation (CV)0.66031187
Kurtosis-0.29896717
Mean16270.536
Median Absolute Deviation (MAD)7804.5
Skewness0.59149577
Sum911150
Variance1.1542554 × 108
MonotonicityNot monotonic
2023-12-12T22:12:29.752780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44341 1
 
1.8%
16934 1
 
1.8%
31444 1
 
1.8%
14092 1
 
1.8%
16329 1
 
1.8%
4027 1
 
1.8%
6595 1
 
1.8%
8175 1
 
1.8%
14069 1
 
1.8%
3026 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
2202 1
1.8%
2286 1
1.8%
2559 1
1.8%
2876 1
1.8%
3026 1
1.8%
3033 1
1.8%
3120 1
1.8%
3493 1
1.8%
3735 1
1.8%
4027 1
1.8%
ValueCountFrequency (%)
44341 1
1.8%
37827 1
1.8%
37213 1
1.8%
34570 1
1.8%
34331 1
1.8%
33399 1
1.8%
31444 1
1.8%
31389 1
1.8%
29166 1
1.8%
26456 1
1.8%

보충역
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2443.9107
Minimum232
Maximum7885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T22:12:29.899324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum232
5-th percentile386.25
Q1949.75
median2259.5
Q33218.25
95-th percentile5505
Maximum7885
Range7653
Interquartile range (IQR)2268.5

Descriptive statistics

Standard deviation1740.4318
Coefficient of variation (CV)0.7121503
Kurtosis0.8386875
Mean2443.9107
Median Absolute Deviation (MAD)1223.5
Skewness0.95868584
Sum136859
Variance3029102.7
MonotonicityNot monotonic
2023-12-12T22:12:30.039443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7885 1
 
1.8%
2097 1
 
1.8%
4274 1
 
1.8%
1892 1
 
1.8%
2402 1
 
1.8%
541 1
 
1.8%
797 1
 
1.8%
1388 1
 
1.8%
1671 1
 
1.8%
406 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
232 1
1.8%
299 1
1.8%
372 1
1.8%
391 1
1.8%
406 1
1.8%
463 1
1.8%
487 1
1.8%
504 1
1.8%
541 1
1.8%
635 1
1.8%
ValueCountFrequency (%)
7885 1
1.8%
6749 1
1.8%
6039 1
1.8%
5327 1
1.8%
5052 1
1.8%
4925 1
1.8%
4654 1
1.8%
4274 1
1.8%
4092 1
1.8%
4056 1
1.8%

전시근로역
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean533.80357
Minimum50
Maximum1566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T22:12:30.185334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile88.5
Q1216.5
median510
Q3730.25
95-th percentile1222.25
Maximum1566
Range1516
Interquartile range (IQR)513.75

Descriptive statistics

Standard deviation367.00901
Coefficient of variation (CV)0.6875357
Kurtosis0.22958413
Mean533.80357
Median Absolute Deviation (MAD)259.5
Skewness0.77172662
Sum29893
Variance134695.62
MonotonicityNot monotonic
2023-12-12T22:12:30.462047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 2
 
3.6%
451 2
 
3.6%
1566 1
 
1.8%
489 1
 
1.8%
723 1
 
1.8%
1129 1
 
1.8%
470 1
 
1.8%
539 1
 
1.8%
100 1
 
1.8%
183 1
 
1.8%
Other values (44) 44
78.6%
ValueCountFrequency (%)
50 1
1.8%
63 1
1.8%
72 1
1.8%
94 2
3.6%
96 1
1.8%
100 1
1.8%
103 1
1.8%
107 1
1.8%
139 1
1.8%
158 1
1.8%
ValueCountFrequency (%)
1566 1
1.8%
1355 1
1.8%
1337 1
1.8%
1184 1
1.8%
1129 1
1.8%
1104 1
1.8%
1047 1
1.8%
938 1
1.8%
899 1
1.8%
848 1
1.8%

병역면제
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.785714
Minimum1
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T22:12:30.672033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q120.75
median60
Q389
95-th percentile149.5
Maximum225
Range224
Interquartile range (IQR)68.25

Descriptive statistics

Standard deviation46.481165
Coefficient of variation (CV)0.72870807
Kurtosis1.3805986
Mean63.785714
Median Absolute Deviation (MAD)37.5
Skewness0.98729863
Sum3572
Variance2160.4987
MonotonicityNot monotonic
2023-12-12T22:12:30.823473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
17 3
 
5.4%
60 2
 
3.6%
71 2
 
3.6%
47 2
 
3.6%
21 2
 
3.6%
59 2
 
3.6%
16 2
 
3.6%
51 2
 
3.6%
18 2
 
3.6%
83 2
 
3.6%
Other values (33) 35
62.5%
ValueCountFrequency (%)
1 1
 
1.8%
2 1
 
1.8%
7 2
3.6%
9 1
 
1.8%
13 1
 
1.8%
16 2
3.6%
17 3
5.4%
18 2
3.6%
20 1
 
1.8%
21 2
3.6%
ValueCountFrequency (%)
225 1
1.8%
157 1
1.8%
154 1
1.8%
148 1
1.8%
133 1
1.8%
128 1
1.8%
122 1
1.8%
107 1
1.8%
104 1
1.8%
101 1
1.8%

재신체검사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean485.32143
Minimum28
Maximum1549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T22:12:31.303533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile50.5
Q1125.25
median435
Q3622.25
95-th percentile1284
Maximum1549
Range1521
Interquartile range (IQR)497

Descriptive statistics

Standard deviation386.79551
Coefficient of variation (CV)0.79698832
Kurtosis0.60799827
Mean485.32143
Median Absolute Deviation (MAD)250.5
Skewness1.0674544
Sum27178
Variance149610.77
MonotonicityNot monotonic
2023-12-12T22:12:31.505932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1172 1
 
1.8%
437 1
 
1.8%
1305 1
 
1.8%
698 1
 
1.8%
420 1
 
1.8%
93 1
 
1.8%
105 1
 
1.8%
233 1
 
1.8%
380 1
 
1.8%
84 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
28 1
1.8%
32 1
1.8%
37 1
1.8%
55 1
1.8%
64 1
1.8%
79 1
1.8%
84 1
1.8%
92 1
1.8%
93 1
1.8%
103 1
1.8%
ValueCountFrequency (%)
1549 1
1.8%
1424 1
1.8%
1305 1
1.8%
1277 1
1.8%
1222 1
1.8%
1172 1
1.8%
1152 1
1.8%
1055 1
1.8%
783 1
1.8%
727 1
1.8%

Interactions

2023-12-12T22:12:27.978823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:24.524377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:25.449034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:26.085984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:26.705526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:27.365047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:28.071490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:24.604705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:25.557092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:26.202023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:26.796959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:27.450623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:28.173491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:24.681573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:25.656795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:26.299902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:26.908550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:27.537108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:28.295021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:24.785267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:25.765749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:26.411130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:27.030205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:27.663436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:28.412625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:24.881490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:25.890630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:26.519413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:27.135009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:27.790755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:28.535130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:24.978208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:25.980150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:26.605176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:27.233860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:12:27.885783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:12:31.638655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도지방청처분인원현역보충역전시근로역병역면제재신체검사
연도1.0000.0000.0000.0000.0000.0000.0000.000
지방청0.0001.0000.8370.8710.6470.8330.6890.810
처분인원0.0000.8371.0000.9980.9620.9830.8970.814
현역0.0000.8710.9981.0000.9550.9830.8920.813
보충역0.0000.6470.9620.9551.0000.9720.9100.778
전시근로역0.0000.8330.9830.9830.9721.0000.9080.799
병역면제0.0000.6890.8970.8920.9100.9081.0000.797
재신체검사0.0000.8100.8140.8130.7780.7990.7971.000
2023-12-12T22:12:31.750719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지방청연도
지방청1.0000.000
연도0.0001.000
2023-12-12T22:12:31.862082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분인원현역보충역전시근로역병역면제재신체검사연도지방청
처분인원1.0001.0000.9870.9920.9730.8970.0000.516
현역1.0001.0000.9850.9900.9710.8970.0000.572
보충역0.9870.9851.0000.9880.9780.8910.0000.315
전시근로역0.9920.9900.9881.0000.9710.9040.0000.506
병역면제0.9730.9710.9780.9711.0000.8690.0000.360
재신체검사0.8970.8970.8910.9040.8691.0000.0000.484
연도0.0000.0000.0000.0000.0000.0001.0000.000
지방청0.5160.5720.3150.5060.3600.4840.0001.000

Missing values

2023-12-12T22:12:28.687206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:12:28.816079image/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

연도지방청처분인원현역보충역전시근로역병역면제재신체검사
02019서울5518944341788515662251172
12019부산.울산27035219903541809107588
22019대구.경북3224026456405684897783
32019경인4604137213603913551571277
42019광주.전남2231318265290260183462
52019대전.충남2431120041320560987369
62019강원6104495983216018135
72019충북99228151135325936123
82019전북122809854183241157126
92019경남2150417763264753871485
연도지방청처분인원현역보충역전시근로역병역면제재신체검사
462022광주.전남1711814138192645151552
472022대전.충남1941715982236253159483
482022강원44013735463941792
492022충북7426625474919721205
502022전북8650706298924022337
512022경남1621913651167444747400
522022제주34792876391947111
532022인천2787023454289276388673
542022경기북부1841115329206742551539
552022강원영동2541220223250255