Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory85.4 B

Variable types

Numeric9

Dataset

Description부산항에 입항하는 선박의 연도별 톤급별 통계 데이터를 제공합니다.- 톤급별 입항선박, 컨테이너선 비중- 단위 : 척수
Author부산항만공사
URLhttps://www.data.go.kr/data/15055479/fileData.do

Alerts

연도 is highly overall correlated with 5천-1만톤 and 4 other fieldsHigh correlation
5천-1만톤 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
1-2만톤 is highly overall correlated with 5천-1만톤 and 3 other fieldsHigh correlation
2-5만톤 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
5만톤 이상 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
합계 is highly overall correlated with 5천-1만톤 and 4 other fieldsHigh correlation
컨테이너선 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
컨테이너선 비중 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
연도 has unique valuesUnique
5천톤 이하 has unique valuesUnique
5천-1만톤 has unique valuesUnique
1-2만톤 has unique valuesUnique
5만톤 이상 has unique valuesUnique
합계 has unique valuesUnique
컨테이너선 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:50:19.530718
Analysis finished2023-12-12 22:50:26.212705
Duration6.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5
Minimum1993
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T07:50:26.267600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1993
5-th percentile1994.45
Q12000.25
median2007.5
Q32014.75
95-th percentile2020.55
Maximum2022
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.0043852595
Kurtosis-1.2
Mean2007.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum60225
Variance77.5
MonotonicityStrictly increasing
2023-12-13T07:50:26.359625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1993 1
 
3.3%
2009 1
 
3.3%
2022 1
 
3.3%
2021 1
 
3.3%
2020 1
 
3.3%
2019 1
 
3.3%
2018 1
 
3.3%
2017 1
 
3.3%
2016 1
 
3.3%
2015 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1993 1
3.3%
1994 1
3.3%
1995 1
3.3%
1996 1
3.3%
1997 1
3.3%
1998 1
3.3%
1999 1
3.3%
2000 1
3.3%
2001 1
3.3%
2002 1
3.3%
ValueCountFrequency (%)
2022 1
3.3%
2021 1
3.3%
2020 1
3.3%
2019 1
3.3%
2018 1
3.3%
2017 1
3.3%
2016 1
3.3%
2015 1
3.3%
2014 1
3.3%
2013 1
3.3%

5천톤 이하
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11451.267
Minimum5529
Maximum15746
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T07:50:26.458550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5529
5-th percentile6089.6
Q110100.75
median11464
Q312924.25
95-th percentile15275.25
Maximum15746
Range10217
Interquartile range (IQR)2823.5

Descriptive statistics

Standard deviation2666.7571
Coefficient of variation (CV)0.23287879
Kurtosis0.1094837
Mean11451.267
Median Absolute Deviation (MAD)1446.5
Skewness-0.4600784
Sum343538
Variance7111593.7
MonotonicityNot monotonic
2023-12-13T07:50:26.555294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9366 1
 
3.3%
12515 1
 
3.3%
5529 1
 
3.3%
5789 1
 
3.3%
6457 1
 
3.3%
8955 1
 
3.3%
9722 1
 
3.3%
10238 1
 
3.3%
10286 1
 
3.3%
10055 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
5529 1
3.3%
5789 1
3.3%
6457 1
3.3%
8955 1
3.3%
9366 1
3.3%
9722 1
3.3%
9982 1
3.3%
10055 1
3.3%
10238 1
3.3%
10286 1
3.3%
ValueCountFrequency (%)
15746 1
3.3%
15408 1
3.3%
15113 1
3.3%
14767 1
3.3%
14750 1
3.3%
14329 1
3.3%
14289 1
3.3%
12938 1
3.3%
12883 1
3.3%
12515 1
3.3%

5천-1만톤
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4757.2
Minimum1900
Maximum7046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T07:50:26.651215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile2212.85
Q13481.25
median4977
Q36219.75
95-th percentile6762.8
Maximum7046
Range5146
Interquartile range (IQR)2738.5

Descriptive statistics

Standard deviation1645.3033
Coefficient of variation (CV)0.3458554
Kurtosis-1.2594847
Mean4757.2
Median Absolute Deviation (MAD)1437.5
Skewness-0.28493208
Sum142716
Variance2707022.9
MonotonicityNot monotonic
2023-12-13T07:50:26.748844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1900 1
 
3.3%
4562 1
 
3.3%
6646 1
 
3.3%
6678 1
 
3.3%
7046 1
 
3.3%
6788 1
 
3.3%
6650 1
 
3.3%
6732 1
 
3.3%
6649 1
 
3.3%
6287 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1900 1
3.3%
2153 1
3.3%
2286 1
3.3%
2516 1
3.3%
2601 1
3.3%
2683 1
3.3%
2765 1
3.3%
3423 1
3.3%
3656 1
3.3%
3982 1
3.3%
ValueCountFrequency (%)
7046 1
3.3%
6788 1
3.3%
6732 1
3.3%
6678 1
3.3%
6650 1
3.3%
6649 1
3.3%
6646 1
3.3%
6287 1
3.3%
6018 1
3.3%
5880 1
3.3%

1-2만톤
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3372.9333
Minimum1730
Maximum3988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T07:50:26.843453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile2322.1
Q13103
median3590
Q33811.25
95-th percentile3954.1
Maximum3988
Range2258
Interquartile range (IQR)708.25

Descriptive statistics

Standard deviation607.0432
Coefficient of variation (CV)0.17997486
Kurtosis0.48631253
Mean3372.9333
Median Absolute Deviation (MAD)294.5
Skewness-1.1667185
Sum101188
Variance368501.44
MonotonicityNot monotonic
2023-12-13T07:50:26.954697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1730 1
 
3.3%
3505 1
 
3.3%
3252 1
 
3.3%
3080 1
 
3.3%
3462 1
 
3.3%
3694 1
 
3.3%
3953 1
 
3.3%
3988 1
 
3.3%
3724 1
 
3.3%
3720 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1730 1
3.3%
2233 1
3.3%
2431 1
3.3%
2455 1
3.3%
2521 1
3.3%
2595 1
3.3%
2987 1
3.3%
3080 1
3.3%
3172 1
3.3%
3252 1
3.3%
ValueCountFrequency (%)
3988 1
3.3%
3955 1
3.3%
3953 1
3.3%
3937 1
3.3%
3912 1
3.3%
3857 1
3.3%
3855 1
3.3%
3816 1
3.3%
3797 1
3.3%
3791 1
3.3%

2-5만톤
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2532.3667
Minimum1735
Maximum2918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T07:50:27.091202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1735
5-th percentile1982.55
Q12433.5
median2618
Q32735.75
95-th percentile2910.15
Maximum2918
Range1183
Interquartile range (IQR)302.25

Descriptive statistics

Standard deviation297.72569
Coefficient of variation (CV)0.11756816
Kurtosis0.57759348
Mean2532.3667
Median Absolute Deviation (MAD)164.5
Skewness-1.0155825
Sum75971
Variance88640.585
MonotonicityNot monotonic
2023-12-13T07:50:27.216917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2769 2
 
6.7%
1735 1
 
3.3%
1965 1
 
3.3%
2514 1
 
3.3%
2668 1
 
3.3%
2450 1
 
3.3%
2594 1
 
3.3%
2779 1
 
3.3%
2700 1
 
3.3%
2658 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
1735 1
3.3%
1965 1
3.3%
2004 1
3.3%
2092 1
3.3%
2196 1
3.3%
2293 1
3.3%
2327 1
3.3%
2431 1
3.3%
2441 1
3.3%
2442 1
3.3%
ValueCountFrequency (%)
2918 1
3.3%
2916 1
3.3%
2903 1
3.3%
2790 1
3.3%
2779 1
3.3%
2769 2
6.7%
2739 1
3.3%
2726 1
3.3%
2715 1
3.3%
2700 1
3.3%

5만톤 이상
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2395.3667
Minimum352
Maximum4534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T07:50:27.345297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum352
5-th percentile416.85
Q1778.5
median2260
Q33681.25
95-th percentile4468.55
Maximum4534
Range4182
Interquartile range (IQR)2902.75

Descriptive statistics

Standard deviation1523.5574
Coefficient of variation (CV)0.63604348
Kurtosis-1.6069045
Mean2395.3667
Median Absolute Deviation (MAD)1476.5
Skewness0.017468815
Sum71861
Variance2321227
MonotonicityNot monotonic
2023-12-13T07:50:27.497911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
352 1
 
3.3%
2765 1
 
3.3%
3534 1
 
3.3%
3572 1
 
3.3%
4100 1
 
3.3%
4469 1
 
3.3%
4534 1
 
3.3%
4456 1
 
3.3%
4468 1
 
3.3%
4289 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
352 1
3.3%
393 1
3.3%
446 1
3.3%
556 1
3.3%
563 1
3.3%
604 1
3.3%
632 1
3.3%
735 1
3.3%
909 1
3.3%
1169 1
3.3%
ValueCountFrequency (%)
4534 1
3.3%
4469 1
3.3%
4468 1
3.3%
4456 1
3.3%
4289 1
3.3%
4100 1
3.3%
3922 1
3.3%
3688 1
3.3%
3661 1
3.3%
3572 1
3.3%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24509.133
Minimum15083
Maximum28719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T07:50:27.608317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15083
5-th percentile17495.3
Q121657.25
median26370
Q327844.5
95-th percentile28454.25
Maximum28719
Range13636
Interquartile range (IQR)6187.25

Descriptive statistics

Standard deviation4066.3214
Coefficient of variation (CV)0.16591045
Kurtosis-0.61197892
Mean24509.133
Median Absolute Deviation (MAD)1802
Skewness-0.84433405
Sum735274
Variance16534970
MonotonicityNot monotonic
2023-12-13T07:50:27.760280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
15083 1
 
3.3%
26041 1
 
3.3%
21730 1
 
3.3%
21633 1
 
3.3%
23834 1
 
3.3%
26574 1
 
3.3%
27309 1
 
3.3%
28008 1
 
3.3%
27906 1
 
3.3%
27051 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
15083 1
3.3%
16859 1
3.3%
18273 1
3.3%
18980 1
3.3%
19310 1
3.3%
19366 1
3.3%
20566 1
3.3%
21633 1
3.3%
21730 1
3.3%
21875 1
3.3%
ValueCountFrequency (%)
28719 1
3.3%
28551 1
3.3%
28336 1
3.3%
28008 1
3.3%
27943 1
3.3%
27906 1
3.3%
27877 1
3.3%
27855 1
3.3%
27813 1
3.3%
27798 1
3.3%

컨테이너선
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12123.333
Minimum6209
Maximum15566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T07:50:27.901216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6209
5-th percentile7071.4
Q110341.5
median12697
Q314300
95-th percentile15331.8
Maximum15566
Range9357
Interquartile range (IQR)3958.5

Descriptive statistics

Standard deviation2765.6161
Coefficient of variation (CV)0.22812341
Kurtosis-0.55927181
Mean12123.333
Median Absolute Deviation (MAD)1880
Skewness-0.76901884
Sum363700
Variance7648632.4
MonotonicityNot monotonic
2023-12-13T07:50:28.018048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6209 1
 
3.3%
11999 1
 
3.3%
12627 1
 
3.3%
12594 1
 
3.3%
13949 1
 
3.3%
14762 1
 
3.3%
15279 1
 
3.3%
15566 1
 
3.3%
15375 1
 
3.3%
15141 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
6209 1
3.3%
6886 1
3.3%
7298 1
3.3%
7984 1
3.3%
8467 1
3.3%
8606 1
3.3%
9419 1
3.3%
10199 1
3.3%
10769 1
3.3%
11625 1
3.3%
ValueCountFrequency (%)
15566 1
3.3%
15375 1
3.3%
15279 1
3.3%
15141 1
3.3%
14762 1
3.3%
14606 1
3.3%
14548 1
3.3%
14417 1
3.3%
13949 1
3.3%
13899 1
3.3%

컨테이너선 비중
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.066667
Minimum40
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T07:50:28.435146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile41
Q146
median47.5
Q354.5
95-th percentile58
Maximum59
Range19
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.6929075
Coefficient of variation (CV)0.11602393
Kurtosis-1.1141007
Mean49.066667
Median Absolute Deviation (MAD)4
Skewness0.24149968
Sum1472
Variance32.409195
MonotonicityNot monotonic
2023-12-13T07:50:28.554520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
46 7
23.3%
56 4
13.3%
41 3
10.0%
44 2
 
6.7%
48 2
 
6.7%
52 2
 
6.7%
58 2
 
6.7%
40 1
 
3.3%
45 1
 
3.3%
47 1
 
3.3%
Other values (5) 5
16.7%
ValueCountFrequency (%)
40 1
 
3.3%
41 3
10.0%
44 2
 
6.7%
45 1
 
3.3%
46 7
23.3%
47 1
 
3.3%
48 2
 
6.7%
49 1
 
3.3%
51 1
 
3.3%
52 2
 
6.7%
ValueCountFrequency (%)
59 1
 
3.3%
58 2
6.7%
56 4
13.3%
55 1
 
3.3%
53 1
 
3.3%
52 2
6.7%
51 1
 
3.3%
49 1
 
3.3%
48 2
6.7%
47 1
 
3.3%

Interactions

2023-12-13T07:50:25.341870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:19.783297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.550045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.232838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.045915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.768867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.589914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.134540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.722949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.436464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:19.881870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.620279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.307825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.135334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.833738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.648083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.200128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.799919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.505925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:19.995248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.691560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.377912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.241516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.898086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.710858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.264849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.862174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.576015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.093063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.776216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.450175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.355085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.961414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.768205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.329466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.926204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.642682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.164978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.868449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.517182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.425676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.024264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.823694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.388264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.986182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.714988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.240236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.942831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.612917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.498185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.088966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.883096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.452903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.051038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.779777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.305925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.006599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.712212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.561059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.147049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.935661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.510564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.113204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.851890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.400383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.082832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.838609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.630085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.462514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.000162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.577508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.184378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.935864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:20.473278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.155513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:21.944636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:22.701038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:23.521361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.060362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:24.643524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:50:25.254266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:50:28.651680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도5천톤 이하5천-1만톤1-2만톤2-5만톤5만톤 이상합계컨테이너선컨테이너선 비중
연도1.0000.8340.9420.6800.6950.8550.7300.9170.866
5천톤 이하0.8341.0000.7540.4350.0000.8180.2800.6320.633
5천-1만톤0.9420.7541.0000.7810.6080.9060.6690.8930.697
1-2만톤0.6800.4350.7811.0000.6290.0000.9610.8100.411
2-5만톤0.6950.0000.6080.6291.0000.0000.6960.7690.000
5만톤 이상0.8550.8180.9060.0000.0001.0000.0000.8570.673
합계0.7300.2800.6690.9610.6960.0001.0000.7470.288
컨테이너선0.9170.6320.8930.8100.7690.8570.7471.0000.820
컨테이너선 비중0.8660.6330.6970.4110.0000.6730.2880.8201.000
2023-12-13T07:50:28.804313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도5천톤 이하5천-1만톤1-2만톤2-5만톤5만톤 이상합계컨테이너선컨테이너선 비중
연도1.000-0.3900.9820.4830.5820.9440.4980.8130.956
5천톤 이하-0.3901.000-0.3640.411-0.034-0.3150.442-0.087-0.447
5천-1만톤0.982-0.3641.0000.5310.5680.9640.5580.8730.952
1-2만톤0.4830.4110.5311.0000.3410.5900.8890.7450.403
2-5만톤0.582-0.0340.5680.3411.0000.5600.5180.5780.613
5만톤 이상0.944-0.3150.9640.5900.5601.0000.6110.9290.900
합계0.4980.4420.5580.8890.5180.6111.0000.8010.437
컨테이너선0.813-0.0870.8730.7450.5780.9290.8011.0000.797
컨테이너선 비중0.956-0.4470.9520.4030.6130.9000.4370.7971.000

Missing values

2023-12-13T07:50:26.039099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:50:26.160809image/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

연도5천톤 이하5천-1만톤1-2만톤2-5만톤5만톤 이상합계컨테이너선컨테이너선 비중
01993936619001730173535215083620941
11994998222862233196539316859688641
219951107521532595200444618273729840
319961154126832431209256319310798441
419971111525162521219663218980846745
519981126426012455244260419366860644
619991153227652987272655620566941946
7200011629342331722916735218751019947
8200112938365633372516909233561076946
92002147673982395522931169261661162544
연도5천톤 이하5천-1만톤1-2만톤2-5만톤5만톤 이상합계컨테이너선컨테이너선 비중
202013113966018367227903922277981454852
212014104625880346726583688261551389953
222015100556287372027004289270511514156
232016102866649372427794468279061537555
242017102386732398825944456280081556656
25201897226650395324504534273091527956
26201989556788369426684469265741476256
27202064577046346227694100238341394959
28202157896678308025143572216331259458
29202255296646325227693534217301262758