Overview

Dataset statistics

Number of variables35
Number of observations49
Missing cells48
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 KiB
Average record size in memory311.7 B

Variable types

Numeric23
Text3
Categorical7
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001055

Alerts

WAVE_AVE_VE_9M has constant value ""Constant
WAVE_AVE_VE_10M_ABOVE has constant value ""Constant
WAVE_MAX_VE_9M has constant value ""Constant
WAVE_MAX_VE_10M_ABOVE has constant value ""Constant
WAVE_AVE_VE_8M is highly imbalanced (78.5%)Imbalance
WAVE_MAX_VE_8M is highly imbalanced (78.5%)Imbalance
SHIP_OWNER_NM has 24 (49.0%) missing valuesMissing
SHPYRD_NM has 24 (49.0%) missing valuesMissing
MMSI has unique valuesUnique
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM has unique valuesUnique
WAVE_AVE_VE_1M has unique valuesUnique
WAVE_AVE_VE_2M has unique valuesUnique
WAVE_AVE_VE_3M has unique valuesUnique
WAVE_MAX_VE_1M has unique valuesUnique
WAVE_MAX_VE_2M has unique valuesUnique
WAVE_MAX_VE_3M has unique valuesUnique
RN has unique valuesUnique
WAVE_AVE_VE_4M has 10 (20.4%) zerosZeros
WAVE_AVE_VE_5M has 21 (42.9%) zerosZeros
WAVE_AVE_VE_6M has 37 (75.5%) zerosZeros
WAVE_AVE_VE_7M has 44 (89.8%) zerosZeros
WAVE_MAX_VE_4M has 10 (20.4%) zerosZeros
WAVE_MAX_VE_5M has 21 (42.9%) zerosZeros
WAVE_MAX_VE_6M has 37 (75.5%) zerosZeros
WAVE_MAX_VE_7M has 44 (89.8%) zerosZeros

Reproduction

Analysis started2023-12-10 14:49:28.469541
Analysis finished2023-12-10 14:49:28.829040
Duration0.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MMSI
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1570559 × 108
Minimum2.15459 × 108
Maximum2.15895 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:28.908788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.15459 × 108
5-th percentile2.15477 × 108
Q12.15639 × 108
median2.15714 × 108
Q32.15805 × 108
95-th percentile2.158794 × 108
Maximum2.15895 × 108
Range436000
Interquartile range (IQR)166000

Descriptive statistics

Standard deviation134501.23
Coefficient of variation (CV)0.00062354075
Kurtosis-0.91946668
Mean2.1570559 × 108
Median Absolute Deviation (MAD)88000
Skewness-0.43385124
Sum1.0569574 × 1010
Variance1.809058 × 1010
MonotonicityStrictly increasing
2023-12-10T23:49:29.069665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
215459000 1
 
2.0%
215822000 1
 
2.0%
215737000 1
 
2.0%
215738000 1
 
2.0%
215773000 1
 
2.0%
215776000 1
 
2.0%
215787000 1
 
2.0%
215790000 1
 
2.0%
215793000 1
 
2.0%
215794000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
215459000 1
2.0%
215465000 1
2.0%
215475000 1
2.0%
215480000 1
2.0%
215486000 1
2.0%
215489000 1
2.0%
215496000 1
2.0%
215517000 1
2.0%
215521000 1
2.0%
215522000 1
2.0%
ValueCountFrequency (%)
215895000 1
2.0%
215887000 1
2.0%
215881000 1
2.0%
215877000 1
2.0%
215874000 1
2.0%
215873000 1
2.0%
215867000 1
2.0%
215853000 1
2.0%
215851000 1
2.0%
215850000 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9530896.3
Minimum9158458
Maximum9879088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:29.217675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9158458
5-th percentile9219868.2
Q19363003
median9544346
Q39626687
95-th percentile9850696.2
Maximum9879088
Range720630
Interquartile range (IQR)263684

Descriptive statistics

Standard deviation205226.98
Coefficient of variation (CV)0.02153281
Kurtosis-0.90913319
Mean9530896.3
Median Absolute Deviation (MAD)145256
Skewness0.10728734
Sum4.6701392 × 108
Variance4.2118113 × 1010
MonotonicityNot monotonic
2023-12-10T23:49:29.372927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9840661 1
 
2.0%
9537642 1
 
2.0%
9465708 1
 
2.0%
9480538 1
 
2.0%
9856725 1
 
2.0%
9158458 1
 
2.0%
9363003 1
 
2.0%
9304215 1
 
2.0%
9552824 1
 
2.0%
9399090 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9158458 1
2.0%
9189770 1
2.0%
9214331 1
2.0%
9228174 1
2.0%
9275957 1
2.0%
9278741 1
2.0%
9288332 1
2.0%
9288344 1
2.0%
9299604 1
2.0%
9304215 1
2.0%
ValueCountFrequency (%)
9879088 1
2.0%
9865374 1
2.0%
9856725 1
2.0%
9841653 1
2.0%
9841641 1
2.0%
9840661 1
2.0%
9835795 1
2.0%
9835783 1
2.0%
9830604 1
2.0%
9738789 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:49:29.628067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length14
Mean length9.7755102
Min length4

Characters and Unicode

Total characters479
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st rowKavokamili
2nd rowLINA AKSOY
3rd rowBUZLUDJA
4th rowSeapower I
5th rowMED PAKIZE
ValueCountFrequency (%)
ii 2
 
2.6%
jiangsu 2
 
2.6%
newyangzi 2
 
2.6%
fin 2
 
2.6%
med 2
 
2.6%
arrow 1
 
1.3%
sky 1
 
1.3%
mighty 1
 
1.3%
alliance 1
 
1.3%
lady 1
 
1.3%
Other values (61) 61
80.3%
2023-12-10T23:49:30.065502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 36
 
7.5%
a 33
 
6.9%
27
 
5.6%
I 26
 
5.4%
S 25
 
5.2%
E 21
 
4.4%
r 19
 
4.0%
i 17
 
3.5%
N 16
 
3.3%
n 16
 
3.3%
Other values (46) 243
50.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 257
53.7%
Lowercase Letter 172
35.9%
Space Separator 27
 
5.6%
Decimal Number 21
 
4.4%
Dash Punctuation 2
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 36
14.0%
I 26
 
10.1%
S 25
 
9.7%
E 21
 
8.2%
N 16
 
6.2%
L 14
 
5.4%
T 12
 
4.7%
O 10
 
3.9%
Y 10
 
3.9%
C 9
 
3.5%
Other values (14) 78
30.4%
Lowercase Letter
ValueCountFrequency (%)
a 33
19.2%
r 19
11.0%
i 17
9.9%
n 16
9.3%
e 15
8.7%
o 12
 
7.0%
l 10
 
5.8%
t 8
 
4.7%
s 6
 
3.5%
m 5
 
2.9%
Other values (12) 31
18.0%
Decimal Number
ValueCountFrequency (%)
0 5
23.8%
2 4
19.0%
1 3
14.3%
3 3
14.3%
7 2
 
9.5%
5 2
 
9.5%
6 1
 
4.8%
4 1
 
4.8%
Space Separator
ValueCountFrequency (%)
27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 429
89.6%
Common 50
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 36
 
8.4%
a 33
 
7.7%
I 26
 
6.1%
S 25
 
5.8%
E 21
 
4.9%
r 19
 
4.4%
i 17
 
4.0%
N 16
 
3.7%
n 16
 
3.7%
e 15
 
3.5%
Other values (36) 205
47.8%
Common
ValueCountFrequency (%)
27
54.0%
0 5
 
10.0%
2 4
 
8.0%
1 3
 
6.0%
3 3
 
6.0%
7 2
 
4.0%
- 2
 
4.0%
5 2
 
4.0%
6 1
 
2.0%
4 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 36
 
7.5%
a 33
 
6.9%
27
 
5.6%
I 26
 
5.4%
S 25
 
5.2%
E 21
 
4.4%
r 19
 
4.0%
i 17
 
3.5%
N 16
 
3.3%
n 16
 
3.3%
Other values (46) 243
50.7%

SHIP_KIND
Categorical

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
Bulk Carrier
30 
BULK CARRIER
13 
Chemical/Oil Product
5 
General Cargo Ship
 
1

Length

Max length20
Median length12
Mean length12.938776
Min length12

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowBulk Carrier
2nd rowBulk Carrier
3rd rowBulk Carrier
4th rowBulk Carrier
5th rowChemical/Oil Product

Common Values

ValueCountFrequency (%)
Bulk Carrier 30
61.2%
BULK CARRIER 13
26.5%
Chemical/Oil Product 5
 
10.2%
General Cargo Ship 1
 
2.0%

Length

2023-12-10T23:49:30.224114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:30.360385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
bulk 43
43.4%
carrier 43
43.4%
chemical/oil 5
 
5.1%
product 5
 
5.1%
general 1
 
1.0%
cargo 1
 
1.0%
ship 1
 
1.0%

SHIP_WDTH
Real number (ℝ)

Distinct17
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.260816
Minimum16
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:30.481036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile17
Q131.94
median32.26
Q332.26
95-th percentile45
Maximum46
Range30
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation7.5510297
Coefficient of variation (CV)0.23406195
Kurtosis0.52744015
Mean32.260816
Median Absolute Deviation (MAD)0.26
Skewness-0.1913309
Sum1580.78
Variance57.018049
MonotonicityNot monotonic
2023-12-10T23:49:30.591517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
32.26 16
32.7%
45.0 5
 
10.2%
17.0 4
 
8.2%
32.24 4
 
8.2%
30.0 3
 
6.1%
32.0 3
 
6.1%
32.2 2
 
4.1%
31.94 2
 
4.1%
27.8 2
 
4.1%
38.0 1
 
2.0%
Other values (7) 7
14.3%
ValueCountFrequency (%)
16.0 1
 
2.0%
17.0 4
8.2%
21.0 1
 
2.0%
27.0 1
 
2.0%
27.8 2
4.1%
30.0 3
6.1%
31.94 2
4.1%
32.0 3
6.1%
32.2 2
4.1%
32.24 4
8.2%
ValueCountFrequency (%)
46.0 1
 
2.0%
45.0 5
 
10.2%
44.98 1
 
2.0%
43.0 1
 
2.0%
38.0 1
 
2.0%
36.8 1
 
2.0%
32.26 16
32.7%
32.24 4
 
8.2%
32.2 2
 
4.1%
32.0 3
 
6.1%

SHIP_LNTH
Real number (ℝ)

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204.25143
Minimum110
Maximum285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:30.720315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile123.25
Q1182
median217
Q3222.5
95-th percentile280.928
Maximum285
Range175
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation45.081843
Coefficient of variation (CV)0.22071739
Kurtosis-0.19572064
Mean204.25143
Median Absolute Deviation (MAD)31.36
Skewness-0.06706263
Sum10008.32
Variance2032.3725
MonotonicityNot monotonic
2023-12-10T23:49:30.871984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
217.0 4
 
8.2%
222.5 4
 
8.2%
172.0 3
 
6.1%
279.0 2
 
4.1%
185.64 2
 
4.1%
182.0 2
 
4.1%
123.25 2
 
4.1%
218.0 2
 
4.1%
190.0 2
 
4.1%
225.0 1
 
2.0%
Other values (25) 25
51.0%
ValueCountFrequency (%)
110.0 1
 
2.0%
121.55 1
 
2.0%
123.25 2
4.1%
124.0 1
 
2.0%
141.0 1
 
2.0%
158.0 1
 
2.0%
166.0 1
 
2.0%
172.0 3
6.1%
178.02 1
 
2.0%
182.0 2
4.1%
ValueCountFrequency (%)
285.0 1
2.0%
282.2 1
2.0%
282.0 1
2.0%
279.32 1
2.0%
279.0 2
4.1%
278.0 1
2.0%
245.62 1
2.0%
225.5 1
2.0%
225.0 1
2.0%
224.0 1
2.0%

SHIP_HGHT
Real number (ℝ)

Distinct28
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.402049
Minimum8.6903
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:31.052470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.6903
5-th percentile10.05014
Q117
median19.8
Q320.25
95-th percentile50
Maximum50
Range41.3097
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation10.610772
Coefficient of variation (CV)0.49578302
Kurtosis3.2733488
Mean21.402049
Median Absolute Deviation (MAD)2.8
Skewness1.9099068
Sum1048.7004
Variance112.58849
MonotonicityNot monotonic
2023-12-10T23:49:31.190761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20.2 6
 
12.2%
50.0 5
 
10.2%
19.9 4
 
8.2%
19.6 3
 
6.1%
14.4 3
 
6.1%
24.8 2
 
4.1%
18.0 2
 
4.1%
10.1204 2
 
4.1%
17.0 2
 
4.1%
17.8 2
 
4.1%
Other values (18) 18
36.7%
ValueCountFrequency (%)
8.6903 1
 
2.0%
8.8946 1
 
2.0%
10.0033 1
 
2.0%
10.1204 2
4.1%
10.8014 1
 
2.0%
13.3 1
 
2.0%
14.33 1
 
2.0%
14.4 3
6.1%
15.6 1
 
2.0%
17.0 2
4.1%
ValueCountFrequency (%)
50.0 5
10.2%
24.8 2
 
4.1%
24.75 1
 
2.0%
24.4 1
 
2.0%
24.2 1
 
2.0%
24.15 1
 
2.0%
24.1 1
 
2.0%
20.25 1
 
2.0%
20.2 6
12.2%
20.0 1
 
2.0%

SHIP_OWNER_NM
Text

MISSING 

Distinct16
Distinct (%)64.0%
Missing24
Missing (%)49.0%
Memory size524.0 B
2023-12-10T23:49:31.362066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length14.12
Min length8

Characters and Unicode

Total characters353
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)48.0%

Sample

1st rowMinerva Marine
2nd rowAston Enterprise
3rd rowEastern Med
4th rowSterling Svea
5th rowMitsubishi Ore
ValueCountFrequency (%)
marine 5
 
10.4%
ciner 4
 
8.3%
cardiff 4
 
8.3%
denizcilik 4
 
8.3%
centrofin 3
 
6.2%
management 3
 
6.2%
eastern 2
 
4.2%
med 2
 
4.2%
shipping 1
 
2.1%
besiktas 1
 
2.1%
Other values (19) 19
39.6%
2023-12-10T23:49:31.674106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 43
12.2%
n 39
 
11.0%
e 37
 
10.5%
r 29
 
8.2%
a 24
 
6.8%
23
 
6.5%
t 14
 
4.0%
s 13
 
3.7%
M 13
 
3.7%
f 11
 
3.1%
Other values (30) 107
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 277
78.5%
Uppercase Letter 52
 
14.7%
Space Separator 23
 
6.5%
Other Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 43
15.5%
n 39
14.1%
e 37
13.4%
r 29
10.5%
a 24
8.7%
t 14
 
5.1%
s 13
 
4.7%
f 11
 
4.0%
o 10
 
3.6%
l 9
 
3.2%
Other values (12) 48
17.3%
Uppercase Letter
ValueCountFrequency (%)
M 13
25.0%
C 11
21.2%
S 5
 
9.6%
D 5
 
9.6%
E 3
 
5.8%
G 3
 
5.8%
A 2
 
3.8%
B 2
 
3.8%
O 1
 
1.9%
P 1
 
1.9%
Other values (6) 6
11.5%
Space Separator
ValueCountFrequency (%)
23
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 329
93.2%
Common 24
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 43
13.1%
n 39
11.9%
e 37
 
11.2%
r 29
 
8.8%
a 24
 
7.3%
t 14
 
4.3%
s 13
 
4.0%
M 13
 
4.0%
f 11
 
3.3%
C 11
 
3.3%
Other values (28) 95
28.9%
Common
ValueCountFrequency (%)
23
95.8%
. 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 43
12.2%
n 39
 
11.0%
e 37
 
10.5%
r 29
 
8.2%
a 24
 
6.8%
23
 
6.5%
t 14
 
4.0%
s 13
 
3.7%
M 13
 
3.7%
f 11
 
3.1%
Other values (30) 107
30.3%

DRAFT
Real number (ℝ)

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.376441
Minimum5.5
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:31.807983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile17.43672
Q123.3859
median30
Q330
95-th percentile30
Maximum30
Range24.5
Interquartile range (IQR)6.6141

Descriptive statistics

Standard deviation5.4500115
Coefficient of variation (CV)0.20662422
Kurtosis3.1000109
Mean26.376441
Median Absolute Deviation (MAD)0
Skewness-1.6885894
Sum1292.4456
Variance29.702625
MonotonicityNot monotonic
2023-12-10T23:49:32.209836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
30.0 28
57.1%
22.5134 2
 
4.1%
17.5276 1
 
2.0%
17.5968 1
 
2.0%
24.4472 1
 
2.0%
5.5 1
 
2.0%
20.7522 1
 
2.0%
17.3956 1
 
2.0%
17.4028 1
 
2.0%
20.429 1
 
2.0%
Other values (11) 11
 
22.4%
ValueCountFrequency (%)
5.5 1
2.0%
17.3956 1
2.0%
17.4028 1
2.0%
17.4876 1
2.0%
17.5276 1
2.0%
17.5519 1
2.0%
17.5968 1
2.0%
20.429 1
2.0%
20.7522 1
2.0%
22.3565 1
2.0%
ValueCountFrequency (%)
30.0 28
57.1%
28.9183 1
 
2.0%
27.55 1
 
2.0%
27.5477 1
 
2.0%
27.5367 1
 
2.0%
26.2934 1
 
2.0%
24.4472 1
 
2.0%
23.8754 1
 
2.0%
23.8642 1
 
2.0%
23.3859 1
 
2.0%

SHPYRD_NM
Text

MISSING 

Distinct17
Distinct (%)68.0%
Missing24
Missing (%)49.0%
Memory size524.0 B
2023-12-10T23:49:32.401977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length15.04
Min length7

Characters and Unicode

Total characters376
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)48.0%

Sample

1st rowSasebo HI
2nd rowHudong Shipyard
3rd rowImabari SB Marugame
4th rowSPP Sacheon SY
5th rowOshima Shipbuilding
ValueCountFrequency (%)
hyundai 6
 
10.5%
hudong 4
 
7.0%
hi 4
 
7.0%
mipo 4
 
7.0%
sy 3
 
5.3%
zhonghua 3
 
5.3%
shipbuilding 3
 
5.3%
sb 3
 
5.3%
shipyard 2
 
3.5%
sacheon 2
 
3.5%
Other values (19) 23
40.4%
2023-12-10T23:49:32.723550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 43
 
11.4%
i 33
 
8.8%
32
 
8.5%
n 27
 
7.2%
u 22
 
5.9%
S 21
 
5.6%
h 20
 
5.3%
o 20
 
5.3%
g 17
 
4.5%
d 15
 
4.0%
Other values (29) 126
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 267
71.0%
Uppercase Letter 75
 
19.9%
Space Separator 32
 
8.5%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 43
16.1%
i 33
12.4%
n 27
10.1%
u 22
8.2%
h 20
 
7.5%
o 20
 
7.5%
g 17
 
6.4%
d 15
 
5.6%
e 10
 
3.7%
y 9
 
3.4%
Other values (10) 51
19.1%
Uppercase Letter
ValueCountFrequency (%)
S 21
28.0%
H 14
18.7%
M 7
 
9.3%
I 6
 
8.0%
B 4
 
5.3%
P 4
 
5.3%
Y 3
 
4.0%
O 3
 
4.0%
Z 3
 
4.0%
D 2
 
2.7%
Other values (6) 8
 
10.7%
Space Separator
ValueCountFrequency (%)
32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 342
91.0%
Common 34
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 43
 
12.6%
i 33
 
9.6%
n 27
 
7.9%
u 22
 
6.4%
S 21
 
6.1%
h 20
 
5.8%
o 20
 
5.8%
g 17
 
5.0%
d 15
 
4.4%
H 14
 
4.1%
Other values (26) 110
32.2%
Common
ValueCountFrequency (%)
32
94.1%
( 1
 
2.9%
) 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 43
 
11.4%
i 33
 
8.8%
32
 
8.5%
n 27
 
7.2%
u 22
 
5.9%
S 21
 
5.6%
h 20
 
5.3%
o 20
 
5.3%
g 17
 
4.5%
d 15
 
4.0%
Other values (29) 126
33.5%

BULD_YR
Real number (ℝ)

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.4898
Minimum2000
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:32.869116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12008
median2011
Q32017
95-th percentile2020
Maximum2020
Range20
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.1682063
Coefficient of variation (CV)0.0030664865
Kurtosis-0.89675236
Mean2011.4898
Median Absolute Deviation (MAD)6
Skewness-0.070081292
Sum98563
Variance38.046769
MonotonicityNot monotonic
2023-12-10T23:49:33.025682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2020 11
22.4%
2011 7
14.3%
2012 5
10.2%
2010 5
10.2%
2004 4
 
8.2%
2005 4
 
8.2%
2001 2
 
4.1%
2014 2
 
4.1%
2009 2
 
4.1%
2000 2
 
4.1%
Other values (5) 5
10.2%
ValueCountFrequency (%)
2000 2
 
4.1%
2001 2
 
4.1%
2004 4
8.2%
2005 4
8.2%
2008 1
 
2.0%
2009 2
 
4.1%
2010 5
10.2%
2011 7
14.3%
2012 5
10.2%
2013 1
 
2.0%
ValueCountFrequency (%)
2020 11
22.4%
2019 1
 
2.0%
2017 1
 
2.0%
2015 1
 
2.0%
2014 2
 
4.1%
2013 1
 
2.0%
2012 5
10.2%
2011 7
14.3%
2010 5
10.2%
2009 2
 
4.1%

DDWGHT
Real number (ℝ)

Distinct43
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73050.878
Minimum6300
Maximum180099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:33.171243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6300
5-th percentile8296.8
Q145000
median74432
Q381827
95-th percentile176131.4
Maximum180099
Range173799
Interquartile range (IQR)36827

Descriptive statistics

Standard deviation48793.716
Coefficient of variation (CV)0.66794155
Kurtosis0.44162011
Mean73050.878
Median Absolute Deviation (MAD)22086
Skewness0.97519788
Sum3579493
Variance2.3808267 × 109
MonotonicityNot monotonic
2023-12-10T23:49:33.312389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
45000 4
 
8.2%
56780 2
 
4.1%
8400 2
 
4.1%
52346 2
 
4.1%
85141 1
 
2.0%
175013 1
 
2.0%
9400 1
 
2.0%
27174 1
 
2.0%
176877 1
 
2.0%
76752 1
 
2.0%
Other values (33) 33
67.3%
ValueCountFrequency (%)
6300 1
2.0%
6600 1
2.0%
8228 1
2.0%
8400 2
4.1%
9400 1
2.0%
27174 1
2.0%
33773 1
2.0%
35058 1
2.0%
35072 1
2.0%
35075 1
2.0%
ValueCountFrequency (%)
180099 1
2.0%
178006 1
2.0%
176877 1
2.0%
175013 1
2.0%
171061 1
2.0%
170726 1
2.0%
169263 1
2.0%
106498 1
2.0%
87328 1
2.0%
85141 1
2.0%

DPTR_HMS
Date

Distinct10
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 06:00:00
Maximum2022-01-25 18:00:00
2023-12-10T23:49:33.417521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:33.751808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

ARVL_HMS
Date

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-05-27 06:00:00
Maximum2022-07-17 18:00:00
2023-12-10T23:49:33.942878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:34.053363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

WAVE_AVE_VE_1M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2651645
Minimum1.56304
Maximum9.42013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:34.177810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.56304
5-th percentile2.737624
Q14.48493
median5.29731
Q36.23656
95-th percentile7.07852
Maximum9.42013
Range7.85709
Interquartile range (IQR)1.75163

Descriptive statistics

Standard deviation1.4180647
Coefficient of variation (CV)0.26932961
Kurtosis1.1065027
Mean5.2651645
Median Absolute Deviation (MAD)0.8291
Skewness-0.074388431
Sum257.99306
Variance2.0109075
MonotonicityNot monotonic
2023-12-10T23:49:34.332388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
6.27691 1
 
2.0%
6.5075 1
 
2.0%
4.44899 1
 
2.0%
6.39402 1
 
2.0%
2.81302 1
 
2.0%
4.48471 1
 
2.0%
7.00094 1
 
2.0%
9.42013 1
 
2.0%
4.92292 1
 
2.0%
6.63323 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1.56304 1
2.0%
2.32473 1
2.0%
2.68736 1
2.0%
2.81302 1
2.0%
3.49911 1
2.0%
3.78858 1
2.0%
3.82111 1
2.0%
4.12895 1
2.0%
4.3102 1
2.0%
4.39437 1
2.0%
ValueCountFrequency (%)
9.42013 1
2.0%
7.39724 1
2.0%
7.13024 1
2.0%
7.00094 1
2.0%
6.98892 1
2.0%
6.69338 1
2.0%
6.68123 1
2.0%
6.63323 1
2.0%
6.5075 1
2.0%
6.46245 1
2.0%

WAVE_AVE_VE_2M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8937273
Minimum2.80786
Maximum10.9022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:34.479826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.80786
5-th percentile2.923248
Q15.266
median6.87319
Q38.39065
95-th percentile10.1696
Maximum10.9022
Range8.09434
Interquartile range (IQR)3.12465

Descriptive statistics

Standard deviation2.1546005
Coefficient of variation (CV)0.31254507
Kurtosis-0.69108561
Mean6.8937273
Median Absolute Deviation (MAD)1.60719
Skewness-0.17711608
Sum337.79264
Variance4.6423033
MonotonicityNot monotonic
2023-12-10T23:49:34.619976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8.5827 1
 
2.0%
4.52308 1
 
2.0%
6.20078 1
 
2.0%
8.39065 1
 
2.0%
4.99887 1
 
2.0%
5.8736 1
 
2.0%
6.80637 1
 
2.0%
9.00677 1
 
2.0%
9.95026 1
 
2.0%
10.9022 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2.80786 1
2.0%
2.87463 1
2.0%
2.9065 1
2.0%
2.94837 1
2.0%
3.50566 1
2.0%
4.28438 1
2.0%
4.29791 1
2.0%
4.52308 1
2.0%
4.87793 1
2.0%
4.89607 1
2.0%
ValueCountFrequency (%)
10.9022 1
2.0%
10.3582 1
2.0%
10.2736 1
2.0%
10.0136 1
2.0%
9.95026 1
2.0%
9.61263 1
2.0%
9.43316 1
2.0%
9.30906 1
2.0%
9.13061 1
2.0%
9.0339 1
2.0%

WAVE_AVE_VE_3M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7523475
Minimum0.833586
Maximum11.8773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:34.769517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.833586
5-th percentile2.121986
Q14.81211
median7.04029
Q38.74767
95-th percentile10.37164
Maximum11.8773
Range11.043714
Interquartile range (IQR)3.93556

Descriptive statistics

Standard deviation2.6539636
Coefficient of variation (CV)0.3930431
Kurtosis-0.55188439
Mean6.7523475
Median Absolute Deviation (MAD)1.94907
Skewness-0.32115399
Sum330.86503
Variance7.0435227
MonotonicityNot monotonic
2023-12-10T23:49:34.920959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
6.03355 1
 
2.0%
4.33432 1
 
2.0%
7.22867 1
 
2.0%
9.77082 1
 
2.0%
2.64626 1
 
2.0%
8.40124 1
 
2.0%
7.04029 1
 
2.0%
5.55575 1
 
2.0%
10.3954 1
 
2.0%
10.0301 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.833586 1
2.0%
1.30703 1
2.0%
1.77247 1
2.0%
2.64626 1
2.0%
2.94238 1
2.0%
3.30064 1
2.0%
3.73251 1
2.0%
3.79092 1
2.0%
4.08398 1
2.0%
4.33432 1
2.0%
ValueCountFrequency (%)
11.8773 1
2.0%
11.0365 1
2.0%
10.3954 1
2.0%
10.336 1
2.0%
10.0301 1
2.0%
9.77082 1
2.0%
9.5968 1
2.0%
9.45936 1
2.0%
9.2968 1
2.0%
9.27609 1
2.0%

WAVE_AVE_VE_4M
Real number (ℝ)

ZEROS 

Distinct40
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4656642
Minimum0
Maximum11.3293
Zeros10
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:35.100535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.728938
median5.53174
Q39.61882
95-th percentile10.98306
Maximum11.3293
Range11.3293
Interquartile range (IQR)8.889882

Descriptive statistics

Standard deviation4.133572
Coefficient of variation (CV)0.7562799
Kurtosis-1.5066984
Mean5.4656642
Median Absolute Deviation (MAD)4.23917
Skewness-0.079793427
Sum267.81755
Variance17.086417
MonotonicityNot monotonic
2023-12-10T23:49:35.224578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 10
 
20.4%
11.2229 1
 
2.0%
10.8807 1
 
2.0%
4.24792 1
 
2.0%
5.19494 1
 
2.0%
3.31125 1
 
2.0%
9.77091 1
 
2.0%
10.2159 1
 
2.0%
10.5732 1
 
2.0%
8.17532 1
 
2.0%
Other values (30) 30
61.2%
ValueCountFrequency (%)
0.0 10
20.4%
0.0137471 1
 
2.0%
0.25513 1
 
2.0%
0.728938 1
 
2.0%
1.94891 1
 
2.0%
2.4554 1
 
2.0%
2.62302 1
 
2.0%
3.31125 1
 
2.0%
3.58801 1
 
2.0%
3.66219 1
 
2.0%
ValueCountFrequency (%)
11.3293 1
2.0%
11.2229 1
2.0%
11.0513 1
2.0%
10.8807 1
2.0%
10.8677 1
2.0%
10.6037 1
2.0%
10.5732 1
2.0%
10.2159 1
2.0%
10.139 1
2.0%
10.0692 1
2.0%

WAVE_AVE_VE_5M
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7035994
Minimum0
Maximum12.2062
Zeros21
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:35.350325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.156706
Q37.78448
95-th percentile10.61742
Maximum12.2062
Range12.2062
Interquartile range (IQR)7.78448

Descriptive statistics

Standard deviation4.3742075
Coefficient of variation (CV)1.1810693
Kurtosis-1.3902749
Mean3.7035994
Median Absolute Deviation (MAD)0.156706
Skewness0.5695585
Sum181.47637
Variance19.133691
MonotonicityNot monotonic
2023-12-10T23:49:35.466082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 21
42.9%
5.66459 1
 
2.0%
2.34082 1
 
2.0%
8.89364 1
 
2.0%
10.488 1
 
2.0%
9.29194 1
 
2.0%
7.67716 1
 
2.0%
0.00108502 1
 
2.0%
8.76607 1
 
2.0%
11.4739 1
 
2.0%
Other values (19) 19
38.8%
ValueCountFrequency (%)
0.0 21
42.9%
0.00108502 1
 
2.0%
0.00829308 1
 
2.0%
0.00961891 1
 
2.0%
0.156706 1
 
2.0%
0.319248 1
 
2.0%
2.14094 1
 
2.0%
2.34082 1
 
2.0%
4.41395 1
 
2.0%
5.01414 1
 
2.0%
ValueCountFrequency (%)
12.2062 1
2.0%
11.4739 1
2.0%
10.7037 1
2.0%
10.488 1
2.0%
10.3072 1
2.0%
10.2954 1
2.0%
10.0397 1
2.0%
9.29194 1
2.0%
8.89364 1
2.0%
8.76607 1
2.0%

WAVE_AVE_VE_6M
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9165189
Minimum0
Maximum11.7015
Zeros37
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:35.576073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10.22154
Maximum11.7015
Range11.7015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.7611136
Coefficient of variation (CV)1.9624715
Kurtosis1.1419003
Mean1.9165189
Median Absolute Deviation (MAD)0
Skewness1.6669368
Sum93.909424
Variance14.145976
MonotonicityNot monotonic
2023-12-10T23:49:35.684637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 37
75.5%
10.0647 1
 
2.0%
11.3146 1
 
2.0%
9.27254 1
 
2.0%
4.46237 1
 
2.0%
5.41001 1
 
2.0%
10.3261 1
 
2.0%
7.81098 1
 
2.0%
9.14515 1
 
2.0%
4.66019 1
 
2.0%
Other values (3) 3
 
6.1%
ValueCountFrequency (%)
0.0 37
75.5%
0.561094 1
 
2.0%
4.46237 1
 
2.0%
4.66019 1
 
2.0%
5.41001 1
 
2.0%
7.81098 1
 
2.0%
9.14515 1
 
2.0%
9.18019 1
 
2.0%
9.27254 1
 
2.0%
10.0647 1
 
2.0%
ValueCountFrequency (%)
11.7015 1
2.0%
11.3146 1
2.0%
10.3261 1
2.0%
10.0647 1
2.0%
9.27254 1
2.0%
9.18019 1
2.0%
9.14515 1
2.0%
7.81098 1
2.0%
5.41001 1
2.0%
4.66019 1
2.0%

WAVE_AVE_VE_7M
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49205116
Minimum0
Maximum11.5302
Zeros44
Zeros (%)89.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:35.788949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.81865
Maximum11.5302
Range11.5302
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0669832
Coefficient of variation (CV)4.2007486
Kurtosis20.249431
Mean0.49205116
Median Absolute Deviation (MAD)0
Skewness4.4656807
Sum24.110507
Variance4.2724195
MonotonicityNot monotonic
2023-12-10T23:49:35.906222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 44
89.8%
4.68919 1
 
2.0%
0.01284 1
 
2.0%
11.5302 1
 
2.0%
7.87436 1
 
2.0%
0.00391682 1
 
2.0%
ValueCountFrequency (%)
0.0 44
89.8%
0.00391682 1
 
2.0%
0.01284 1
 
2.0%
4.68919 1
 
2.0%
7.87436 1
 
2.0%
11.5302 1
 
2.0%
ValueCountFrequency (%)
11.5302 1
 
2.0%
7.87436 1
 
2.0%
4.68919 1
 
2.0%
0.01284 1
 
2.0%
0.00391682 1
 
2.0%
0.0 44
89.8%

WAVE_AVE_VE_8M
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
46 
6.03895
 
1
0.645468
 
1
0.000852432
 
1

Length

Max length11
Median length3
Mean length3.3469388
Min length3

Unique

Unique3 ?
Unique (%)6.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46
93.9%
6.03895 1
 
2.0%
0.645468 1
 
2.0%
0.000852432 1
 
2.0%

Length

2023-12-10T23:49:36.063073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:36.208014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46
93.9%
6.03895 1
 
2.0%
0.645468 1
 
2.0%
0.000852432 1
 
2.0%

WAVE_AVE_VE_9M
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
100.0%

Length

2023-12-10T23:49:36.314551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:36.424584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

WAVE_AVE_VE_10M_ABOVE
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
100.0%

Length

2023-12-10T23:49:36.519700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:36.622749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

WAVE_MAX_VE_1M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.129295
Minimum9.99355
Maximum17.0928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:36.753877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.99355
5-th percentile12.68264
Q113.5809
median14.136
Q314.6675
95-th percentile15.92212
Maximum17.0928
Range7.09925
Interquartile range (IQR)1.0866

Descriptive statistics

Standard deviation1.1613501
Coefficient of variation (CV)0.082194481
Kurtosis2.9614931
Mean14.129295
Median Absolute Deviation (MAD)0.5515
Skewness-0.55757018
Sum692.33545
Variance1.348734
MonotonicityNot monotonic
2023-12-10T23:49:36.906843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
16.4356 1
 
2.0%
12.6857 1
 
2.0%
12.7659 1
 
2.0%
13.79 1
 
2.0%
9.99355 1
 
2.0%
14.0556 1
 
2.0%
11.8032 1
 
2.0%
14.3999 1
 
2.0%
13.3875 1
 
2.0%
14.5857 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9.99355 1
2.0%
11.8032 1
2.0%
12.6806 1
2.0%
12.6857 1
2.0%
12.7659 1
2.0%
12.9349 1
2.0%
13.1484 1
2.0%
13.1591 1
2.0%
13.2386 1
2.0%
13.2854 1
2.0%
ValueCountFrequency (%)
17.0928 1
2.0%
16.4356 1
2.0%
16.0682 1
2.0%
15.703 1
2.0%
15.5358 1
2.0%
15.372 1
2.0%
15.0229 1
2.0%
14.9893 1
2.0%
14.9509 1
2.0%
14.9268 1
2.0%

WAVE_MAX_VE_2M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.602432
Minimum9.21869
Maximum16.2753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:37.052720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.21869
5-th percentile12.07444
Q112.8726
median13.5575
Q314.2828
95-th percentile15.47732
Maximum16.2753
Range7.05661
Interquartile range (IQR)1.4102

Descriptive statistics

Standard deviation1.2079041
Coefficient of variation (CV)0.088800596
Kurtosis2.6649278
Mean13.602432
Median Absolute Deviation (MAD)0.7157
Skewness-0.69665346
Sum666.51919
Variance1.4590323
MonotonicityNot monotonic
2023-12-10T23:49:37.217018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
15.8337 1
 
2.0%
11.4055 1
 
2.0%
12.435 1
 
2.0%
13.7585 1
 
2.0%
9.21869 1
 
2.0%
13.2215 1
 
2.0%
11.9256 1
 
2.0%
14.5095 1
 
2.0%
12.4066 1
 
2.0%
14.2732 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9.21869 1
2.0%
11.4055 1
2.0%
11.9256 1
2.0%
12.2977 1
2.0%
12.3908 1
2.0%
12.4066 1
2.0%
12.435 1
2.0%
12.5067 1
2.0%
12.5564 1
2.0%
12.7042 1
2.0%
ValueCountFrequency (%)
16.2753 1
2.0%
15.8337 1
2.0%
15.716 1
2.0%
15.1193 1
2.0%
14.9326 1
2.0%
14.8799 1
2.0%
14.8167 1
2.0%
14.7866 1
2.0%
14.5102 1
2.0%
14.5095 1
2.0%

WAVE_MAX_VE_3M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.63757
Minimum8.4714
Maximum14.8421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:37.377383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.4714
5-th percentile9.645562
Q112.1155
median12.6914
Q313.9901
95-th percentile14.28146
Maximum14.8421
Range6.3707
Interquartile range (IQR)1.8746

Descriptive statistics

Standard deviation1.4842731
Coefficient of variation (CV)0.11744925
Kurtosis0.51156631
Mean12.63757
Median Absolute Deviation (MAD)1.0772
Skewness-0.93319518
Sum619.24092
Variance2.2030667
MonotonicityNot monotonic
2023-12-10T23:49:37.524742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
13.3317 1
 
2.0%
10.728 1
 
2.0%
12.3461 1
 
2.0%
13.3931 1
 
2.0%
9.38827 1
 
2.0%
12.6914 1
 
2.0%
11.7871 1
 
2.0%
14.3453 1
 
2.0%
12.3451 1
 
2.0%
13.9901 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
8.4714 1
2.0%
9.11445 1
2.0%
9.38827 1
2.0%
10.0315 1
2.0%
10.625 1
2.0%
10.728 1
2.0%
10.822 1
2.0%
10.866 1
2.0%
11.4012 1
2.0%
11.5397 1
2.0%
ValueCountFrequency (%)
14.8421 1
2.0%
14.4312 1
2.0%
14.3453 1
2.0%
14.1857 1
2.0%
14.1663 1
2.0%
14.1394 1
2.0%
14.1128 1
2.0%
14.1059 1
2.0%
14.0486 1
2.0%
14.0468 1
2.0%

WAVE_MAX_VE_4M
Real number (ℝ)

ZEROS 

Distinct40
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4573667
Minimum0
Maximum13.567
Zeros10
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:38.054576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.62729
median11.2884
Q312.417
95-th percentile13.27294
Maximum13.567
Range13.567
Interquartile range (IQR)9.78971

Descriptive statistics

Standard deviation5.2838181
Coefficient of variation (CV)0.62475925
Kurtosis-1.1062876
Mean8.4573667
Median Absolute Deviation (MAD)1.4359
Skewness-0.85283346
Sum414.41097
Variance27.918734
MonotonicityNot monotonic
2023-12-10T23:49:38.226481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 10
 
20.4%
12.1263 1
 
2.0%
12.4096 1
 
2.0%
12.5614 1
 
2.0%
10.9657 1
 
2.0%
11.9089 1
 
2.0%
12.0203 1
 
2.0%
13.0051 1
 
2.0%
12.6255 1
 
2.0%
11.1643 1
 
2.0%
Other values (30) 30
61.2%
ValueCountFrequency (%)
0.0 10
20.4%
0.0290955 1
 
2.0%
0.728982 1
 
2.0%
2.62729 1
 
2.0%
2.75729 1
 
2.0%
7.88469 1
 
2.0%
8.09234 1
 
2.0%
8.75578 1
 
2.0%
10.1222 1
 
2.0%
10.2297 1
 
2.0%
ValueCountFrequency (%)
13.567 1
2.0%
13.4723 1
2.0%
13.4219 1
2.0%
13.0495 1
2.0%
13.0051 1
2.0%
12.8922 1
2.0%
12.8208 1
2.0%
12.7243 1
2.0%
12.6255 1
2.0%
12.5614 1
2.0%

WAVE_MAX_VE_5M
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9862105
Minimum0
Maximum12.741
Zeros21
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:38.382109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.319291
Q310.8059
95-th percentile12.45566
Maximum12.741
Range12.741
Interquartile range (IQR)10.8059

Descriptive statistics

Standard deviation5.4539965
Coefficient of variation (CV)1.0938159
Kurtosis-1.8598526
Mean4.9862105
Median Absolute Deviation (MAD)0.319291
Skewness0.2636987
Sum244.32431
Variance29.746077
MonotonicityNot monotonic
2023-12-10T23:49:38.547960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 21
42.9%
8.09801 1
 
2.0%
12.7105 1
 
2.0%
9.78689 1
 
2.0%
12.741 1
 
2.0%
11.9565 1
 
2.0%
11.994 1
 
2.0%
0.00146948 1
 
2.0%
11.6234 1
 
2.0%
11.4739 1
 
2.0%
Other values (19) 19
38.8%
ValueCountFrequency (%)
0.0 21
42.9%
0.00146948 1
 
2.0%
0.00830162 1
 
2.0%
0.0124958 1
 
2.0%
0.319291 1
 
2.0%
0.599795 1
 
2.0%
6.89075 1
 
2.0%
7.7798 1
 
2.0%
7.78448 1
 
2.0%
8.09801 1
 
2.0%
ValueCountFrequency (%)
12.741 1
2.0%
12.7105 1
2.0%
12.5415 1
2.0%
12.3269 1
2.0%
12.3047 1
2.0%
11.994 1
2.0%
11.9565 1
2.0%
11.7288 1
2.0%
11.6234 1
2.0%
11.4739 1
2.0%

WAVE_MAX_VE_6M
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4154241
Minimum0
Maximum12.1916
Zeros37
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:38.729650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.37238
Maximum12.1916
Range12.1916
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.4623402
Coefficient of variation (CV)1.8474355
Kurtosis0.069117255
Mean2.4154241
Median Absolute Deviation (MAD)0
Skewness1.3987104
Sum118.35578
Variance19.91248
MonotonicityNot monotonic
2023-12-10T23:49:38.882035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 37
75.5%
11.4109 1
 
2.0%
11.3146 1
 
2.0%
9.45322 1
 
2.0%
9.65751 1
 
2.0%
9.15051 1
 
2.0%
10.9422 1
 
2.0%
9.17457 1
 
2.0%
11.2077 1
 
2.0%
9.31241 1
 
2.0%
Other values (3) 3
 
6.1%
ValueCountFrequency (%)
0.0 37
75.5%
2.44846 1
 
2.0%
9.15051 1
 
2.0%
9.17457 1
 
2.0%
9.31241 1
 
2.0%
9.45322 1
 
2.0%
9.65751 1
 
2.0%
10.9422 1
 
2.0%
11.2077 1
 
2.0%
11.3146 1
 
2.0%
ValueCountFrequency (%)
12.1916 1
2.0%
12.0921 1
2.0%
11.4109 1
2.0%
11.3146 1
2.0%
11.2077 1
2.0%
10.9422 1
2.0%
9.65751 1
2.0%
9.45322 1
2.0%
9.31241 1
2.0%
9.17457 1
2.0%

WAVE_MAX_VE_7M
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60251785
Minimum0
Maximum11.5302
Zeros44
Zeros (%)89.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:39.023867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.201358
Maximum11.5302
Range11.5302
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4014756
Coefficient of variation (CV)3.9857336
Kurtosis14.00087
Mean0.60251785
Median Absolute Deviation (MAD)0
Skewness3.8863242
Sum29.523374
Variance5.7670852
MonotonicityNot monotonic
2023-12-10T23:49:39.133399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 44
89.8%
9.30917 1
 
2.0%
0.01284 1
 
2.0%
11.5302 1
 
2.0%
8.66037 1
 
2.0%
0.0107945 1
 
2.0%
ValueCountFrequency (%)
0.0 44
89.8%
0.0107945 1
 
2.0%
0.01284 1
 
2.0%
8.66037 1
 
2.0%
9.30917 1
 
2.0%
11.5302 1
 
2.0%
ValueCountFrequency (%)
11.5302 1
 
2.0%
9.30917 1
 
2.0%
8.66037 1
 
2.0%
0.01284 1
 
2.0%
0.0107945 1
 
2.0%
0.0 44
89.8%

WAVE_MAX_VE_8M
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
46 
6.03895
 
1
0.645468
 
1
0.000852432
 
1

Length

Max length11
Median length3
Mean length3.3469388
Min length3

Unique

Unique3 ?
Unique (%)6.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46
93.9%
6.03895 1
 
2.0%
0.645468 1
 
2.0%
0.000852432 1
 
2.0%

Length

2023-12-10T23:49:39.266638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:39.408710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46
93.9%
6.03895 1
 
2.0%
0.645468 1
 
2.0%
0.000852432 1
 
2.0%

WAVE_MAX_VE_9M
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
100.0%

Length

2023-12-10T23:49:39.555402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:39.663411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

WAVE_MAX_VE_10M_ABOVE
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
100.0%

Length

2023-12-10T23:49:39.771847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:39.880854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

RN
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:40.015020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q114
median26
Q338
95-th percentile47.6
Maximum50
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.54956501
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)12
Skewness0
Sum1274
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:49:40.191377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

Sample

MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSWAVE_AVE_VE_1MWAVE_AVE_VE_2MWAVE_AVE_VE_3MWAVE_AVE_VE_4MWAVE_AVE_VE_5MWAVE_AVE_VE_6MWAVE_AVE_VE_7MWAVE_AVE_VE_8MWAVE_AVE_VE_9MWAVE_AVE_VE_10M_ABOVEWAVE_MAX_VE_1MWAVE_MAX_VE_2MWAVE_MAX_VE_3MWAVE_MAX_VE_4MWAVE_MAX_VE_5MWAVE_MAX_VE_6MWAVE_MAX_VE_7MWAVE_MAX_VE_8MWAVE_MAX_VE_9MWAVE_MAX_VE_10M_ABOVERN
02154590009840661KavokamiliBulk Carrier38.0225.019.1Minerva Marine30.0Sasebo HI20208514101-Jan-2022 12:00:0005-Jun-2022 18:00:006.276918.58276.0335511.22290.00.00.00.00016.435615.833713.331712.12630.00.00.00.0002
12154650009865374LINA AKSOYBulk Carrier32.0199.950.0<NA>26.2934<NA>20206106801-Jan-2022 12:00:0005-Jun-2022 18:00:006.236564.896075.161916.583545.664590.00.00.00017.092814.879914.001612.01438.098010.00.00.0003
22154750009835783BUZLUDJABulk Carrier31.94189.9650.0<NA>30.0<NA>20194500001-Jan-2022 12:00:0005-Jun-2022 18:00:006.462457.518527.577726.295784.413950.00.00.00013.758112.852913.352211.40486.890750.00.00.0004
32154800009214331Seapower IBulk Carrier32.26217.019.6Aston Enterprise30.0Hudong Shipyard20017466501-Jan-2022 12:00:0005-Jun-2022 18:00:007.130245.2665.580274.029617.5843110.06470.00.00014.219614.236414.185713.5679.7522711.41090.00.0005
42154860009830604MED PAKIZEChemical/Oil Product17.0124.010.0033<NA>22.3565<NA>2020822801-Jan-2022 12:00:0017-Jul-2022 18:00:004.714345.729723.300644.762187.784480.00.00.00014.267912.755412.42317.884697.784480.00.00.0006
52154890009738789Santa IriniBulk Carrier32.24218.6319.9Eastern Med30.0Imabari SB Marugame20157711905-Jan-2022 12:00:0005-Jun-2022 18:00:005.6598610.01369.276090.00.00.00.00.00014.846814.816714.16630.00.00.00.00.0007
62154960009544346ATLAS 014Chemical/Oil Product21.0123.2510.1204<NA>22.5134<NA>2020840001-Jan-2022 06:00:0016-Jul-2022 18:00:003.499112.807865.215647.27870.00.00.00.00012.934913.3112.152111.36760.00.00.00.0008
72155170009588366ALYCIABULK CARRIER30.0172.014.4<NA>27.5367<NA>20123505801-Jan-2022 12:00:0017-Jul-2022 18:00:004.31026.873193.732510.255130.0096190.00.00.00013.238613.557510.8662.757290.0124960.00.00.0009
82155210009595785ADELINABULK CARRIER30.0172.014.4<NA>27.5477<NA>20123507201-Jan-2022 12:00:0017-Jul-2022 18:00:004.128956.402444.812112.623020.00.00.00.00013.999313.732514.84212.627290.00.00.00.00010
92155220009588378ALINDABULK CARRIER30.0172.014.4<NA>27.55<NA>20123507501-Jan-2022 12:00:0017-Jul-2022 18:00:005.38677.668494.801813.588012.1409411.31460.00.00014.989315.71614.431212.49349.895111.31460.00.00011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSWAVE_AVE_VE_1MWAVE_AVE_VE_2MWAVE_AVE_VE_3MWAVE_AVE_VE_4MWAVE_AVE_VE_5MWAVE_AVE_VE_6MWAVE_AVE_VE_7MWAVE_AVE_VE_8MWAVE_AVE_VE_9MWAVE_AVE_VE_10M_ABOVEWAVE_MAX_VE_1MWAVE_MAX_VE_2MWAVE_MAX_VE_3MWAVE_MAX_VE_4MWAVE_MAX_VE_5MWAVE_MAX_VE_6MWAVE_MAX_VE_7MWAVE_MAX_VE_8MWAVE_MAX_VE_9MWAVE_MAX_VE_10M_ABOVERN
392158500009299604CatalinaBulk Carrier32.26217.019.6Dryships30.0Hudong Zhonghua20057443201-Jan-2022 12:00:0017-Jul-2022 00:00:005.794878.140858.566149.852019.2919411.701511.53020.00013.148413.178913.492713.049511.956512.092111.53020.00041
402158510009275957ManasotaBulk Carrier45.0279.3224.1Cardiff Marine17.4028Hyundai HI (Ulsan)200417106101-Jan-2022 12:00:0017-Jul-2022 18:00:006.681237.43039.59688.754090.00.00.00.00015.022913.338713.076111.1110.00.00.00.00042
412158530009228174AlamedaBulk Carrier44.98278.024.15Cardiff Marine17.3956Hyundai Samho HI200117072607-Jan-2022 00:00:0017-Jul-2022 18:00:007.397249.03398.3071110.069210.4889.180197.874360.00015.70312.872613.671312.892212.74112.19168.660370.00043
422158670009879088CEKSAN 73Chemical/Oil Product17.0110.08.8946<NA>20.7522<NA>2020660001-Jan-2022 12:00:0017-Jul-2022 18:00:004.50316.412734.083980.00.00.00.00.00013.42812.390812.28850.00.00.00.00.00044
432158730009324629INTREPIDBULK CARRIER32.26182.017.0<NA>30.0<NA>20055234601-Jan-2022 12:00:0017-Jul-2022 18:00:005.544976.093027.400649.575928.893640.00.00.00013.285413.063712.425110.42859.786890.00.00.00045
442158740009324617COURAGEOUSBULK CARRIER32.26182.017.0<NA>30.0<NA>20055234601-Jan-2022 12:00:0017-Jul-2022 18:00:004.679895.810691.307038.092340.00.00.00.00014.386812.50679.114458.092340.00.00.00.00046
452158770009607277BLUE FINBULK CARRIER32.26185.6418.0<NA>5.5<NA>20115678025-Jan-2022 18:00:0017-Jul-2022 18:00:006.023458.250823.790921.948912.340820.5610940.0039170.0008520015.535814.489214.139412.820812.71052.448460.0107950.0008520047
462158810009675597Saint MyronBulk Carrier32.24218.019.9Eastern Med30.0Imabari SB Marugame20147711601-Jan-2022 12:00:0005-Jun-2022 18:00:006.0638610.35827.610370.00.00.00.00.00014.383814.510213.95620.00.00.00.00.00048
472158870009607289YELLOW FINBULK CARRIER32.26185.6418.0<NA>24.4472<NA>20115678001-Jan-2022 18:00:0017-Jul-2022 00:00:005.003154.297916.8077310.1390.00.00.00.00014.530814.002110.62510.34490.00.00.00.00049
482158950009439606Sealeader IIBulk Carrier46.0285.024.8Thenamaris17.5968Beihai Shipyard201118009901-Jan-2022 12:00:0001-Jun-2022 00:00:004.394372.90652.942380.7289380.00.00.00.00014.926813.872912.21540.7289820.00.00.00.00050