Overview

Dataset statistics

Number of variables12
Number of observations2144
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory220.0 KiB
Average record size in memory105.1 B

Variable types

DateTime1
Categorical2
Numeric9

Dataset

Description선박법에 따라 각 지방해양수산청에서 등록한 선박 통계입니다. 기간은 2014년 01월부터 2019년 07월까지 입니다. 월간, 척수/톤수, 톤급구분별, 총계,5년미만,5-10년미만,10-15년미만,15-20년미만,20-25년미만,25-30년미만,30-35년미만,35년이상 통계화한 정보입니다.
Author해양수산부
URLhttps://www.data.go.kr/data/15055850/fileData.do

Alerts

총계 is highly overall correlated with 5년미만 and 7 other fieldsHigh correlation
5년미만 is highly overall correlated with 총계 and 7 other fieldsHigh correlation
5-10년미만 is highly overall correlated with 총계 and 7 other fieldsHigh correlation
10-15년미만 is highly overall correlated with 총계 and 7 other fieldsHigh correlation
15-20년미만 is highly overall correlated with 총계 and 7 other fieldsHigh correlation
20-25년미만 is highly overall correlated with 총계 and 7 other fieldsHigh correlation
25-30년미만 is highly overall correlated with 총계 and 7 other fieldsHigh correlation
30-35년미만 is highly overall correlated with 총계 and 7 other fieldsHigh correlation
35년이상 is highly overall correlated with 총계 and 7 other fieldsHigh correlation
5년미만 has 74 (3.5%) zerosZeros
5-10년미만 has 100 (4.7%) zerosZeros
10-15년미만 has 134 (6.2%) zerosZeros
15-20년미만 has 38 (1.8%) zerosZeros
25-30년미만 has 104 (4.9%) zerosZeros
30-35년미만 has 278 (13.0%) zerosZeros
35년이상 has 184 (8.6%) zerosZeros

Reproduction

Analysis started2023-12-12 02:38:32.146891
Analysis finished2023-12-12 02:38:42.717744
Duration10.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables


Date

Distinct67
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
Minimum2014-01-01 00:00:00
Maximum2019-07-01 00:00:00
2023-12-12T11:38:42.798281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:42.970193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

톤급구분
Categorical

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
5톤미만
 
134
20톤미만
 
134
30톤미만
 
134
50톤미만
 
134
100톤미만
 
134
Other values (11)
1474 

Length

Max length10
Median length8
Mean length7.125
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5톤미만
2nd row5톤미만
3rd row20톤미만
4th row20톤미만
5th row30톤미만

Common Values

ValueCountFrequency (%)
5톤미만 134
 
6.2%
20톤미만 134
 
6.2%
30톤미만 134
 
6.2%
50톤미만 134
 
6.2%
100톤미만 134
 
6.2%
200톤미만 134
 
6.2%
300톤미만 134
 
6.2%
500톤미만 134
 
6.2%
1,000톤미만 134
 
6.2%
2,000톤미만 134
 
6.2%
Other values (6) 804
37.5%

Length

2023-12-12T11:38:43.179730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5톤미만 134
 
6.2%
20톤미만 134
 
6.2%
30톤미만 134
 
6.2%
50톤미만 134
 
6.2%
100톤미만 134
 
6.2%
200톤미만 134
 
6.2%
300톤미만 134
 
6.2%
500톤미만 134
 
6.2%
1,000톤미만 134
 
6.2%
2,000톤미만 134
 
6.2%
Other values (6) 804
37.5%

구분
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
척수
1072 
톤수
1072 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row척수
2nd row톤수
3rd row척수
4th row톤수
5th row척수

Common Values

ValueCountFrequency (%)
척수 1072
50.0%
톤수 1072
50.0%

Length

2023-12-12T11:38:43.346174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:38:43.474155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
척수 1072
50.0%
톤수 1072
50.0%

총계
Real number (ℝ)

HIGH CORRELATION 

Distinct1346
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3783307.6
Minimum7
Maximum98279311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:38:43.649211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile47
Q1532
median2638.5
Q31372872.5
95-th percentile26840273
Maximum98279311
Range98279304
Interquartile range (IQR)1372340.5

Descriptive statistics

Standard deviation12343885
Coefficient of variation (CV)3.262723
Kurtosis24.299668
Mean3783307.6
Median Absolute Deviation (MAD)2624.5
Skewness4.6876818
Sum8.1114116 × 109
Variance1.5237149 × 1014
MonotonicityNot monotonic
2023-12-12T11:38:43.809820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 20
 
0.9%
79 15
 
0.7%
78 15
 
0.7%
80 14
 
0.7%
573 11
 
0.5%
10 11
 
0.5%
13 10
 
0.5%
77 10
 
0.5%
76 10
 
0.5%
526 10
 
0.5%
Other values (1336) 2018
94.1%
ValueCountFrequency (%)
7 3
 
0.1%
9 7
 
0.3%
10 11
0.5%
11 1
 
< 0.1%
12 6
 
0.3%
13 10
0.5%
14 20
0.9%
15 7
 
0.3%
17 2
 
0.1%
30 2
 
0.1%
ValueCountFrequency (%)
98279311 1
< 0.1%
98275186 1
< 0.1%
95159250 1
< 0.1%
90386339 1
< 0.1%
90385522 2
0.1%
90377012 1
< 0.1%
90373264 1
< 0.1%
83644663 1
< 0.1%
83643219 1
< 0.1%
83609996 1
< 0.1%

5년미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct791
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41631.689
Minimum0
Maximum927062
Zeros74
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:38:43.975021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q131
median137
Q324933
95-th percentile259575.3
Maximum927062
Range927062
Interquartile range (IQR)24902

Descriptive statistics

Standard deviation111456.49
Coefficient of variation (CV)2.6772031
Kurtosis27.344704
Mean41631.689
Median Absolute Deviation (MAD)137
Skewness4.723772
Sum89258342
Variance1.2422548 × 1010
MonotonicityNot monotonic
2023-12-12T11:38:44.129699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
3.5%
1 71
 
3.3%
102545 47
 
2.2%
27 32
 
1.5%
5 29
 
1.4%
30 27
 
1.3%
10 27
 
1.3%
9 25
 
1.2%
50623 24
 
1.1%
35 22
 
1.0%
Other values (781) 1766
82.4%
ValueCountFrequency (%)
0 74
3.5%
1 71
3.3%
2 12
 
0.6%
3 8
 
0.4%
4 2
 
0.1%
5 29
 
1.4%
6 20
 
0.9%
8 10
 
0.5%
9 25
 
1.2%
10 27
 
1.3%
ValueCountFrequency (%)
927062 1
< 0.1%
921029 1
< 0.1%
919069 1
< 0.1%
909062 1
< 0.1%
895043 1
< 0.1%
891074 1
< 0.1%
884043 1
< 0.1%
868033 1
< 0.1%
849074 1
< 0.1%
847065 1
< 0.1%

5-10년미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct892
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76583.212
Minimum0
Maximum1388001
Zeros100
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:38:44.270376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q134
median260.5
Q320899.75
95-th percentile415315.45
Maximum1388001
Range1388001
Interquartile range (IQR)20865.75

Descriptive statistics

Standard deviation186077.83
Coefficient of variation (CV)2.4297471
Kurtosis18.040598
Mean76583.212
Median Absolute Deviation (MAD)260.5
Skewness3.7361196
Sum1.6419441 × 108
Variance3.4624961 × 1010
MonotonicityNot monotonic
2023-12-12T11:38:44.420333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 100
 
4.7%
27 38
 
1.8%
7 32
 
1.5%
26 29
 
1.4%
33 28
 
1.3%
49 27
 
1.3%
35 24
 
1.1%
44 23
 
1.1%
8 23
 
1.1%
45 22
 
1.0%
Other values (882) 1798
83.9%
ValueCountFrequency (%)
0 100
4.7%
1 17
 
0.8%
3 2
 
0.1%
4 16
 
0.7%
5 5
 
0.2%
6 19
 
0.9%
7 32
 
1.5%
8 23
 
1.1%
9 7
 
0.3%
10 5
 
0.2%
ValueCountFrequency (%)
1388001 1
< 0.1%
1376062 1
< 0.1%
1371005 1
< 0.1%
1362029 1
< 0.1%
1357062 1
< 0.1%
1332038 1
< 0.1%
1327091 1
< 0.1%
1320091 1
< 0.1%
1312084 1
< 0.1%
1304091 1
< 0.1%

10-15년미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct858
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61252.477
Minimum0
Maximum1216047
Zeros134
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:38:44.561790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q136
median267.5
Q321580.5
95-th percentile339365
Maximum1216047
Range1216047
Interquartile range (IQR)21544.5

Descriptive statistics

Standard deviation160844.56
Coefficient of variation (CV)2.6259274
Kurtosis21.153668
Mean61252.477
Median Absolute Deviation (MAD)267.5
Skewness4.1816555
Sum1.3132531 × 108
Variance2.5870972 × 1010
MonotonicityNot monotonic
2023-12-12T11:38:44.717845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 134
 
6.2%
3 30
 
1.4%
2 28
 
1.3%
51 27
 
1.3%
32 26
 
1.2%
10 24
 
1.1%
9 23
 
1.1%
54 22
 
1.0%
49 22
 
1.0%
48 20
 
0.9%
Other values (848) 1788
83.4%
ValueCountFrequency (%)
0 134
6.2%
2 28
 
1.3%
3 30
 
1.4%
4 3
 
0.1%
5 5
 
0.2%
6 1
 
< 0.1%
7 5
 
0.2%
8 16
 
0.7%
9 23
 
1.1%
10 24
 
1.1%
ValueCountFrequency (%)
1216047 1
< 0.1%
1196093 1
< 0.1%
1186031 1
< 0.1%
1179085 1
< 0.1%
1179012 1
< 0.1%
1178009 1
< 0.1%
1173078 1
< 0.1%
1173015 1
< 0.1%
1168004 1
< 0.1%
1165059 1
< 0.1%

15-20년미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1078
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102504.01
Minimum0
Maximum1372892
Zeros38
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:38:44.904411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q160
median390
Q346989.5
95-th percentile652907.5
Maximum1372892
Range1372892
Interquartile range (IQR)46929.5

Descriptive statistics

Standard deviation235585.72
Coefficient of variation (CV)2.2983074
Kurtosis9.6309126
Mean102504.01
Median Absolute Deviation (MAD)388
Skewness3.0414519
Sum2.1976859 × 108
Variance5.5500632 × 1010
MonotonicityNot monotonic
2023-12-12T11:38:45.049183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
1.8%
2 34
 
1.6%
59 24
 
1.1%
311755 20
 
0.9%
15 18
 
0.8%
65 16
 
0.7%
63 15
 
0.7%
258827 14
 
0.7%
60 14
 
0.7%
58 14
 
0.7%
Other values (1068) 1937
90.3%
ValueCountFrequency (%)
0 38
1.8%
1 5
 
0.2%
2 34
1.6%
3 14
 
0.7%
4 5
 
0.2%
5 5
 
0.2%
6 11
 
0.5%
7 10
 
0.5%
8 7
 
0.3%
9 7
 
0.3%
ValueCountFrequency (%)
1372892 1
< 0.1%
1351439 2
0.1%
1351044 1
< 0.1%
1291027 1
< 0.1%
1286301 1
< 0.1%
1285719 1
< 0.1%
1270001 1
< 0.1%
1267076 1
< 0.1%
1266054 1
< 0.1%
1265064 1
< 0.1%

20-25년미만
Real number (ℝ)

HIGH CORRELATION 

Distinct1122
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151486.86
Minimum2
Maximum2648081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:38:45.218327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13
Q1106.25
median381.5
Q390859
95-th percentile1143941
Maximum2648081
Range2648079
Interquartile range (IQR)90752.75

Descriptive statistics

Standard deviation385292.46
Coefficient of variation (CV)2.5434051
Kurtosis13.970557
Mean151486.86
Median Absolute Deviation (MAD)374.5
Skewness3.5935538
Sum3.2478783 × 108
Variance1.4845028 × 1011
MonotonicityNot monotonic
2023-12-12T11:38:45.370799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 28
 
1.3%
16 28
 
1.3%
18 26
 
1.2%
135 25
 
1.2%
19 23
 
1.1%
20 19
 
0.9%
133 18
 
0.8%
137 18
 
0.8%
139 16
 
0.7%
9 14
 
0.7%
Other values (1112) 1929
90.0%
ValueCountFrequency (%)
2 8
0.4%
3 10
0.5%
4 7
0.3%
5 4
 
0.2%
6 14
0.7%
7 7
0.3%
8 11
0.5%
9 14
0.7%
10 12
0.6%
11 8
0.4%
ValueCountFrequency (%)
2648081 1
< 0.1%
2638017 1
< 0.1%
2624058 1
< 0.1%
2619037 1
< 0.1%
2556095 1
< 0.1%
2495081 1
< 0.1%
2439068 1
< 0.1%
2402073 1
< 0.1%
2397054 1
< 0.1%
2350006 1
< 0.1%

25-30년미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct914
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66450
Minimum0
Maximum3069002
Zeros104
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:38:45.818251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q165.75
median226
Q340309
95-th percentile170867.3
Maximum3069002
Range3069002
Interquartile range (IQR)40243.25

Descriptive statistics

Standard deviation314201.35
Coefficient of variation (CV)4.7283874
Kurtosis77.20484
Mean66450
Median Absolute Deviation (MAD)226
Skewness8.6098505
Sum1.424688 × 108
Variance9.8722487 × 1010
MonotonicityNot monotonic
2023-12-12T11:38:45.967823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104
 
4.9%
1 55
 
2.6%
4 46
 
2.1%
3 40
 
1.9%
80994 28
 
1.3%
13 27
 
1.3%
5 26
 
1.2%
2 23
 
1.1%
6 22
 
1.0%
115 18
 
0.8%
Other values (904) 1755
81.9%
ValueCountFrequency (%)
0 104
4.9%
1 55
2.6%
2 23
 
1.1%
3 40
 
1.9%
4 46
2.1%
5 26
 
1.2%
6 22
 
1.0%
7 4
 
0.2%
9 4
 
0.2%
10 5
 
0.2%
ValueCountFrequency (%)
3069002 1
< 0.1%
3067015 1
< 0.1%
3051035 1
< 0.1%
3041011 1
< 0.1%
3039084 1
< 0.1%
3031059 1
< 0.1%
3028089 1
< 0.1%
3026027 1
< 0.1%
3024045 1
< 0.1%
3018047 1
< 0.1%

30-35년미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct756
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean550676.2
Minimum0
Maximum59688025
Zeros278
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:38:46.100163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median86
Q321013.25
95-th percentile123907.1
Maximum59688025
Range59688025
Interquartile range (IQR)20994.25

Descriptive statistics

Standard deviation4126299
Coefficient of variation (CV)7.4931495
Kurtosis116.83215
Mean550676.2
Median Absolute Deviation (MAD)86
Skewness10.305514
Sum1.1806498 × 109
Variance1.7026344 × 1013
MonotonicityNot monotonic
2023-12-12T11:38:46.251198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 278
 
13.0%
5 49
 
2.3%
2 39
 
1.8%
6 33
 
1.5%
64963 32
 
1.5%
81907 26
 
1.2%
42 24
 
1.1%
51 21
 
1.0%
3 21
 
1.0%
54 21
 
1.0%
Other values (746) 1600
74.6%
ValueCountFrequency (%)
0 278
13.0%
1 18
 
0.8%
2 39
 
1.8%
3 21
 
1.0%
4 19
 
0.9%
5 49
 
2.3%
6 33
 
1.5%
7 10
 
0.5%
8 5
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
59688025 1
 
< 0.1%
56562025 2
 
0.1%
53844025 1
 
< 0.1%
51362025 1
 
< 0.1%
45002058 5
0.2%
36011079 1
 
< 0.1%
35308079 1
 
< 0.1%
35024079 2
 
0.1%
34661079 1
 
< 0.1%
16285004 1
 
< 0.1%

35년이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct724
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2732723.2
Minimum0
Maximum96975088
Zeros184
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:38:46.431855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median253
Q328298
95-th percentile20472690
Maximum96975088
Range96975088
Interquartile range (IQR)28276

Descriptive statistics

Standard deviation10806017
Coefficient of variation (CV)3.9543035
Kurtosis33.255458
Mean2732723.2
Median Absolute Deviation (MAD)253
Skewness5.3909673
Sum5.8589585 × 109
Variance1.1677 × 1014
MonotonicityNot monotonic
2023-12-12T11:38:46.593773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 184
 
8.6%
1 113
 
5.3%
22723 49
 
2.3%
11710 42
 
2.0%
2 36
 
1.7%
6 35
 
1.6%
15 25
 
1.2%
45294 23
 
1.1%
16 22
 
1.0%
5 21
 
1.0%
Other values (714) 1594
74.3%
ValueCountFrequency (%)
0 184
8.6%
1 113
5.3%
2 36
 
1.7%
3 8
 
0.4%
4 10
 
0.5%
5 21
 
1.0%
6 35
 
1.6%
7 20
 
0.9%
13 2
 
0.1%
14 19
 
0.9%
ValueCountFrequency (%)
96975088 2
 
0.1%
93866084 1
 
< 0.1%
89061088 5
0.2%
82312088 2
 
0.1%
82276088 1
 
< 0.1%
80272088 3
0.1%
79917088 2
 
0.1%
75043088 1
 
< 0.1%
72994088 3
0.1%
70384088 2
 
0.1%

Interactions

2023-12-12T11:38:41.386679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:33.313745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.182447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.020283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.840650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.744875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:38.030390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:39.112513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:40.470230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:41.495196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:33.409602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.288382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.110178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.964114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.855031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:38.149382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:39.228988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:40.565742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:41.593247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:33.494333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.375292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.207176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.051283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.989854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:38.271262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:39.353153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:40.654907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:41.732035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:33.595185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.467361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.295179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.162783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:37.131666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:38.410658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:39.467151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:40.757821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:41.832682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:33.686257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.569026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.392282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.261210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:37.282542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:38.529893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:39.567614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:40.867902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:41.951971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:33.796624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.656498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.481063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.354654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:37.449023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:38.651544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:39.714766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:40.980109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:42.074213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:33.881751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.738219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.568564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.447504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:37.614219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:38.769665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:39.856665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:41.087342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:42.188555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:33.973953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.836929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.657057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.535178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:37.794214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:38.886569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:39.974055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:41.188724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:42.300462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.082169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:34.920089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:35.743983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:36.646036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:37.909476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:39.006447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:40.354139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:38:41.276831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:38:46.715809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
톤급구분구분총계5년미만5-10년미만10-15년미만15-20년미만20-25년미만25-30년미만30-35년미만35년이상
1.0000.0000.0000.2960.0000.0000.0000.0000.0000.0000.0000.197
톤급구분0.0001.0000.0000.5110.6590.7310.6450.6410.6740.6440.4920.545
구분0.0000.0001.0000.3930.3410.6000.4420.6340.5230.2750.1770.271
총계0.2960.5110.3931.0000.4880.5390.5430.5780.6530.6040.7360.955
5년미만0.0000.6590.3410.4881.0000.7580.7510.7440.7600.7390.0000.000
5-10년미만0.0000.7310.6000.5390.7581.0000.8030.8260.8000.9080.0980.304
10-15년미만0.0000.6450.4420.5430.7510.8031.0000.7620.7560.8010.0470.477
15-20년미만0.0000.6410.6340.5780.7440.8260.7621.0000.8900.7520.1090.333
20-25년미만0.0000.6740.5230.6530.7600.8000.7560.8901.0000.7620.0000.348
25-30년미만0.0000.6440.2750.6040.7390.9080.8010.7520.7621.0000.0000.000
30-35년미만0.0000.4920.1770.7360.0000.0980.0470.1090.0000.0001.0000.739
35년이상0.1970.5450.2710.9550.0000.3040.4770.3330.3480.0000.7391.000
2023-12-12T11:38:46.844472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분톤급구분
구분1.0000.000
톤급구분0.0001.000
2023-12-12T11:38:46.938172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총계5년미만5-10년미만10-15년미만15-20년미만20-25년미만25-30년미만30-35년미만35년이상톤급구분구분
총계1.0000.8660.8240.8160.8950.9400.8440.7100.8010.2280.301
5년미만0.8661.0000.7740.8040.9110.8890.7660.7350.6900.3750.364
5-10년미만0.8240.7741.0000.9280.8330.8330.8190.6730.7400.3470.453
10-15년미만0.8160.8040.9281.0000.8780.8080.7960.7400.8060.3310.441
15-20년미만0.8950.9110.8330.8781.0000.9250.7970.6900.7190.3140.491
20-25년미만0.9400.8890.8330.8080.9251.0000.8490.6320.6950.3410.402
25-30년미만0.8440.7660.8190.7960.7970.8491.0000.7660.8070.3520.183
30-35년미만0.7100.7350.6730.7400.6900.6320.7661.0000.8470.1930.133
35년이상0.8010.6900.7400.8060.7190.6950.8070.8471.0000.2590.270
톤급구분0.2280.3750.3470.3310.3140.3410.3520.1930.2591.0000.000
구분0.3010.3640.4530.4410.4910.4020.1830.1330.2700.0001.000

Missing values

2023-12-12T11:38:42.441665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:38:42.640945image/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

톤급구분구분총계5년미만5-10년미만10-15년미만15-20년미만20-25년미만25-30년미만30-35년미만35년이상
02014-015톤미만척수1861255484446344177854228
12014-015톤미만톤수383548489777071050124113894
22014-0120톤미만척수1494116145117221339195164197
32014-0120톤미만톤수1731413791463117124513665224421972744
42014-0130톤미만척수8133738661471328283228
52014-0130톤미만톤수20459877971180138133356201420135614
62014-0150톤미만척수52629292978785664163
72014-0150톤미만톤수2023210311076112629903142214124716255
82014-01100톤미만척수79533265813913075114220
92014-01100톤미만톤수58143248519854386975898965728829515610
톤급구분구분총계5년미만5-10년미만10-15년미만15-20년미만20-25년미만25-30년미만30-35년미만35년이상
21342019-0710,000톤미만척수23723227348451286
21352019-0710,000톤미만톤수1623834171032141163524419312843307912745554661645294
21362019-0720,000톤미만척수76618171018133
21372019-0720,000톤미만톤수3394818289746261033219738151475293059167083738032879043
21382019-0750,000톤미만척수7481918913502
21392019-0750,000톤미만톤수2529591285533656920586468281916501765161203055786
21402019-07100,000톤미만척수33036319002
21412019-07100,000톤미만톤수23836170195647468491208851133128000179348
21422019-07100,000톤이상척수1001032400
21432019-07100,000톤이상톤수13879050102545047859025882754794300