Overview

Dataset statistics

Number of variables22
Number of observations49
Missing cells46
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory194.7 B

Variable types

Numeric16
Text3
Categorical1
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001047

Alerts

SHIP_OWNER_NM has 23 (46.9%) missing valuesMissing
SHPYRD_NM has 23 (46.9%) missing valuesMissing
MMSI has unique valuesUnique
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM has unique valuesUnique
DPTR_HMS has unique valuesUnique
ARVL_HMS has unique valuesUnique
DPTRP_LA has unique valuesUnique
DPTRP_LO has unique valuesUnique
DTNT_LA has unique valuesUnique
DTNT_LO has unique valuesUnique
MAX_VE has unique valuesUnique
AVE_VE has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:25:51.515402
Analysis finished2023-12-10 14:25:51.789139
Duration0.27 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%
Mean2.2945957 × 108
Minimum2.29285 × 108
Maximum2.29614 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:51.862141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.29285 × 108
5-th percentile2.292894 × 108
Q12.29376 × 108
median2.29422 × 108
Q32.29565 × 108
95-th percentile2.296116 × 108
Maximum2.29614 × 108
Range329000
Interquartile range (IQR)189000

Descriptive statistics

Standard deviation109404.83
Coefficient of variation (CV)0.00047679349
Kurtosis-1.4225623
Mean2.2945957 × 108
Median Absolute Deviation (MAD)108000
Skewness0.0047250975
Sum1.1243519 × 1010
Variance1.1969417 × 1010
MonotonicityStrictly increasing
2023-12-10T23:25:52.015129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
229285000 1
 
2.0%
229574000 1
 
2.0%
229486000 1
 
2.0%
229523000 1
 
2.0%
229529000 1
 
2.0%
229530000 1
 
2.0%
229531000 1
 
2.0%
229532000 1
 
2.0%
229533000 1
 
2.0%
229534000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
229285000 1
2.0%
229286000 1
2.0%
229287000 1
2.0%
229293000 1
2.0%
229305000 1
2.0%
229308000 1
2.0%
229342000 1
2.0%
229347000 1
2.0%
229354000 1
2.0%
229361000 1
2.0%
ValueCountFrequency (%)
229614000 1
2.0%
229613000 1
2.0%
229612000 1
2.0%
229611000 1
2.0%
229609000 1
2.0%
229604000 1
2.0%
229603000 1
2.0%
229602000 1
2.0%
229587000 1
2.0%
229585000 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9556146
Minimum9118678
Maximum9750983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:52.177674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9118678
5-th percentile9272729.2
Q19479204
median9617519
Q39662320
95-th percentile9721678.2
Maximum9750983
Range632305
Interquartile range (IQR)183116

Descriptive statistics

Standard deviation156074.97
Coefficient of variation (CV)0.016332418
Kurtosis0.74579863
Mean9556146
Median Absolute Deviation (MAD)56854
Skewness-1.2251741
Sum4.6825116 × 108
Variance2.4359396 × 1010
MonotonicityNot monotonic
2023-12-10T23:25:52.323092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9633410 1
 
2.0%
9587269 1
 
2.0%
9643910 1
 
2.0%
9668403 1
 
2.0%
9618006 1
 
2.0%
9618018 1
 
2.0%
9617519 1
 
2.0%
9617521 1
 
2.0%
9646900 1
 
2.0%
9646895 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9118678 1
2.0%
9153056 1
2.0%
9266140 1
2.0%
9282613 1
2.0%
9286592 1
2.0%
9303144 1
2.0%
9311177 1
2.0%
9323900 1
2.0%
9445772 1
2.0%
9457854 1
2.0%
ValueCountFrequency (%)
9750983 1
2.0%
9750971 1
2.0%
9721683 1
2.0%
9721671 1
2.0%
9718686 1
2.0%
9684213 1
2.0%
9674397 1
2.0%
9674385 1
2.0%
9674373 1
2.0%
9668403 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:25:52.586498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length9.8571429
Min length4

Characters and Unicode

Total characters483
Distinct characters44
Distinct categories3 ?
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 rowMykonos
2nd rowMini
3rd rowDelos
4th rowMinoan Pioneer
5th rowFiji
ValueCountFrequency (%)
la 4
 
5.2%
pola 4
 
5.2%
kiran 3
 
3.9%
thalassini 3
 
3.9%
js 2
 
2.6%
mykonos 1
 
1.3%
gh 1
 
1.3%
astrid 1
 
1.3%
guimorais 1
 
1.3%
briantais 1
 
1.3%
Other values (56) 56
72.7%
2023-12-10T23:25:53.289218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 45
 
9.3%
28
 
5.8%
L 26
 
5.4%
O 25
 
5.2%
I 25
 
5.2%
a 24
 
5.0%
i 23
 
4.8%
n 21
 
4.3%
S 20
 
4.1%
N 18
 
3.7%
Other values (34) 228
47.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 276
57.1%
Lowercase Letter 179
37.1%
Space Separator 28
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 24
13.4%
i 23
12.8%
n 21
11.7%
e 17
9.5%
o 15
8.4%
s 13
 
7.3%
l 10
 
5.6%
h 8
 
4.5%
t 8
 
4.5%
r 6
 
3.4%
Other values (12) 34
19.0%
Uppercase Letter
ValueCountFrequency (%)
A 45
16.3%
L 26
 
9.4%
O 25
 
9.1%
I 25
 
9.1%
S 20
 
7.2%
N 18
 
6.5%
R 17
 
6.2%
G 12
 
4.3%
T 11
 
4.0%
E 11
 
4.0%
Other values (11) 66
23.9%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 455
94.2%
Common 28
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 45
 
9.9%
L 26
 
5.7%
O 25
 
5.5%
I 25
 
5.5%
a 24
 
5.3%
i 23
 
5.1%
n 21
 
4.6%
S 20
 
4.4%
N 18
 
4.0%
e 17
 
3.7%
Other values (33) 211
46.4%
Common
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 45
 
9.3%
28
 
5.8%
L 26
 
5.4%
O 25
 
5.2%
I 25
 
5.2%
a 24
 
5.0%
i 23
 
4.8%
n 21
 
4.3%
S 20
 
4.1%
N 18
 
3.7%
Other values (34) 228
47.2%

SHIP_KIND
Categorical

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Bulk Carrier
25 
BULK CARRIER
23 
Chip Carrier
 
1

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowBulk Carrier
2nd rowBulk Carrier
3rd rowBulk Carrier
4th rowBulk Carrier
5th rowBulk Carrier

Common Values

ValueCountFrequency (%)
Bulk Carrier 25
51.0%
BULK CARRIER 23
46.9%
Chip Carrier 1
 
2.0%

Length

2023-12-10T23:25:53.447837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:25:53.545991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
carrier 49
50.0%
bulk 48
49.0%
chip 1
 
1.0%

SHIP_WDTH
Real number (ℝ)

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.470204
Minimum28.4
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:53.649954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28.4
5-th percentile28.6
Q132
median32.26
Q343
95-th percentile46
Maximum50
Range21.6
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.6102321
Coefficient of variation (CV)0.18636014
Kurtosis-0.69941306
Mean35.470204
Median Absolute Deviation (MAD)2.26
Skewness0.93726435
Sum1738.04
Variance43.695169
MonotonicityNot monotonic
2023-12-10T23:25:53.752306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
32.26 15
30.6%
45.0 8
16.3%
30.0 8
16.3%
32.0 3
 
6.1%
38.0 2
 
4.1%
32.2 2
 
4.1%
28.4 2
 
4.1%
50.0 2
 
4.1%
46.0 2
 
4.1%
28.6 2
 
4.1%
Other values (3) 3
 
6.1%
ValueCountFrequency (%)
28.4 2
 
4.1%
28.6 2
 
4.1%
30.0 8
16.3%
32.0 3
 
6.1%
32.2 2
 
4.1%
32.24 1
 
2.0%
32.26 15
30.6%
36.5 1
 
2.0%
38.0 2
 
4.1%
43.0 1
 
2.0%
ValueCountFrequency (%)
50.0 2
 
4.1%
46.0 2
 
4.1%
45.0 8
16.3%
43.0 1
 
2.0%
38.0 2
 
4.1%
36.5 1
 
2.0%
32.26 15
30.6%
32.24 1
 
2.0%
32.2 2
 
4.1%
32.0 3
 
6.1%

SHIP_LNTH
Real number (ℝ)

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.72408
Minimum168.5
Maximum294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:53.861752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168.5
5-th percentile176.65
Q1185
median217
Q3248
95-th percentile287.404
Maximum294
Range125.5
Interquartile range (IQR)63

Descriptive statistics

Standard deviation42.733348
Coefficient of variation (CV)0.19448641
Kurtosis-1.0550279
Mean219.72408
Median Absolute Deviation (MAD)32
Skewness0.64596632
Sum10766.48
Variance1826.139
MonotonicityNot monotonic
2023-12-10T23:25:53.980115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
217.0 5
 
10.2%
176.75 4
 
8.2%
222.0 4
 
8.2%
185.0 4
 
8.2%
176.65 4
 
8.2%
194.5 3
 
6.1%
225.5 2
 
4.1%
286.4 2
 
4.1%
168.5 2
 
4.1%
286.66 2
 
4.1%
Other values (14) 17
34.7%
ValueCountFrequency (%)
168.5 2
4.1%
176.65 4
8.2%
176.75 4
8.2%
178.0 2
4.1%
185.0 4
8.2%
185.34 1
 
2.0%
193.74 1
 
2.0%
194.5 3
6.1%
199.98 1
 
2.0%
205.0 1
 
2.0%
ValueCountFrequency (%)
294.0 2
4.1%
287.9 1
2.0%
286.66 2
4.1%
286.4 2
4.1%
285.0 2
4.1%
283.8 1
2.0%
282.2 1
2.0%
282.0 1
2.0%
248.0 1
2.0%
225.5 2
4.1%

SHIP_HGHT
Real number (ℝ)

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.478571
Minimum14.25
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:54.098413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.25
5-th percentile14.7
Q117.7
median19
Q320.7
95-th percentile24.9
Maximum24.9
Range10.65
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.5792807
Coefficient of variation (CV)0.18375478
Kurtosis-1.0346511
Mean19.478571
Median Absolute Deviation (MAD)1.7
Skewness0.29695679
Sum954.45
Variance12.81125
MonotonicityNot monotonic
2023-12-10T23:25:54.214753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
24.8 5
 
10.2%
14.7 4
 
8.2%
18.0 4
 
8.2%
15.0 4
 
8.2%
18.5 4
 
8.2%
24.9 4
 
8.2%
20.05 3
 
6.1%
20.7 2
 
4.1%
18.6 2
 
4.1%
19.9 2
 
4.1%
Other values (13) 15
30.6%
ValueCountFrequency (%)
14.25 1
 
2.0%
14.7 4
8.2%
14.8 1
 
2.0%
15.0 4
8.2%
15.6 2
4.1%
17.7 1
 
2.0%
18.0 4
8.2%
18.1 1
 
2.0%
18.5 4
8.2%
18.6 2
4.1%
ValueCountFrequency (%)
24.9 4
8.2%
24.8 5
10.2%
24.75 1
 
2.0%
24.7 1
 
2.0%
24.5 1
 
2.0%
20.7 2
 
4.1%
20.2 1
 
2.0%
20.05 3
6.1%
20.0 1
 
2.0%
19.9 2
 
4.1%

SHIP_OWNER_NM
Text

MISSING 

Distinct18
Distinct (%)69.2%
Missing23
Missing (%)46.9%
Memory size524.0 B
2023-12-10T23:25:54.425654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15.5
Mean length13.153846
Min length8

Characters and Unicode

Total characters342
Distinct characters39
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

Unique13 ?
Unique (%)50.0%

Sample

1st rowSea Traders
2nd rowSea Traders
3rd rowSea Traders
4th rowModion Maritime SA
5th rowSea Traders
ValueCountFrequency (%)
sea 4
 
7.7%
traders 4
 
7.7%
dryships 3
 
5.8%
sa 3
 
5.8%
maritime 3
 
5.8%
ciner 2
 
3.8%
denizcilik 2
 
3.8%
enesel 2
 
3.8%
nyk 2
 
3.8%
blkshp 2
 
3.8%
Other values (24) 25
48.1%
2023-12-10T23:25:54.762348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 31
 
9.1%
i 31
 
9.1%
26
 
7.6%
r 26
 
7.6%
a 24
 
7.0%
s 23
 
6.7%
n 18
 
5.3%
t 16
 
4.7%
l 16
 
4.7%
h 12
 
3.5%
Other values (29) 119
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 256
74.9%
Uppercase Letter 59
 
17.3%
Space Separator 26
 
7.6%
Other Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31
12.1%
i 31
12.1%
r 26
10.2%
a 24
9.4%
s 23
9.0%
n 18
 
7.0%
t 16
 
6.2%
l 16
 
6.2%
h 12
 
4.7%
m 9
 
3.5%
Other values (11) 50
19.5%
Uppercase Letter
ValueCountFrequency (%)
S 10
16.9%
M 9
15.3%
A 8
13.6%
D 5
8.5%
T 5
8.5%
C 4
 
6.8%
E 3
 
5.1%
B 3
 
5.1%
K 2
 
3.4%
Y 2
 
3.4%
Other values (6) 8
13.6%
Space Separator
ValueCountFrequency (%)
26
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 315
92.1%
Common 27
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31
 
9.8%
i 31
 
9.8%
r 26
 
8.3%
a 24
 
7.6%
s 23
 
7.3%
n 18
 
5.7%
t 16
 
5.1%
l 16
 
5.1%
h 12
 
3.8%
S 10
 
3.2%
Other values (27) 108
34.3%
Common
ValueCountFrequency (%)
26
96.3%
. 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31
 
9.1%
i 31
 
9.1%
26
 
7.6%
r 26
 
7.6%
a 24
 
7.0%
s 23
 
6.7%
n 18
 
5.3%
t 16
 
4.7%
l 16
 
4.7%
h 12
 
3.5%
Other values (29) 119
34.8%

DRAFT
Real number (ℝ)

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.799453
Minimum4
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:54.877564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.2
Q117.5907
median27.2549
Q330
95-th percentile30
Maximum30
Range26
Interquartile range (IQR)12.4093

Descriptive statistics

Standard deviation8.3936528
Coefficient of variation (CV)0.3681515
Kurtosis-0.40278248
Mean22.799453
Median Absolute Deviation (MAD)2.7451
Skewness-0.87241885
Sum1117.1732
Variance70.453407
MonotonicityNot monotonic
2023-12-10T23:25:54.992532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
30.0 20
40.8%
29.4548 3
 
6.1%
8.0 2
 
4.1%
4.0 2
 
4.1%
17.5907 2
 
4.1%
9.0 2
 
4.1%
21.0 2
 
4.1%
27.3311 1
 
2.0%
17.6168 1
 
2.0%
17.5947 1
 
2.0%
Other values (13) 13
26.5%
ValueCountFrequency (%)
4.0 2
4.1%
5.0 1
2.0%
8.0 2
4.1%
9.0 2
4.1%
17.49 1
2.0%
17.5141 1
2.0%
17.585 1
2.0%
17.5876 1
2.0%
17.5907 2
4.1%
17.5947 1
2.0%
ValueCountFrequency (%)
30.0 20
40.8%
29.4548 3
 
6.1%
27.3311 1
 
2.0%
27.2549 1
 
2.0%
24.4033 1
 
2.0%
24.3378 1
 
2.0%
24.3353 1
 
2.0%
21.0 2
 
4.1%
18.155 1
 
2.0%
18.1537 1
 
2.0%

SHPYRD_NM
Text

MISSING 

Distinct15
Distinct (%)57.7%
Missing23
Missing (%)46.9%
Memory size524.0 B
2023-12-10T23:25:55.160885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length15.423077
Min length7

Characters and Unicode

Total characters401
Distinct characters40
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

Unique8 ?
Unique (%)30.8%

Sample

1st rowNew Century SB
2nd rowNew Century SB
3rd rowNew Times SB
4th rowJiangsu New YZJ
5th rowNew Century SB
ValueCountFrequency (%)
new 7
 
11.1%
sb 6
 
9.5%
shipbuilding 4
 
6.3%
jiangsu 3
 
4.8%
yzj 3
 
4.8%
tsuneishi 3
 
4.8%
zosen 3
 
4.8%
scs 3
 
4.8%
century 3
 
4.8%
xingang 2
 
3.2%
Other values (20) 26
41.3%
2023-12-10T23:25:55.525362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 44
 
11.0%
37
 
9.2%
n 33
 
8.2%
a 29
 
7.2%
S 25
 
6.2%
e 22
 
5.5%
g 20
 
5.0%
h 18
 
4.5%
u 18
 
4.5%
s 17
 
4.2%
Other values (30) 138
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 271
67.6%
Uppercase Letter 89
 
22.2%
Space Separator 37
 
9.2%
Open Punctuation 2
 
0.5%
Close Punctuation 2
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 44
16.2%
n 33
12.2%
a 29
10.7%
e 22
8.1%
g 20
 
7.4%
h 18
 
6.6%
u 18
 
6.6%
s 17
 
6.3%
o 13
 
4.8%
d 7
 
2.6%
Other values (11) 50
18.5%
Uppercase Letter
ValueCountFrequency (%)
S 25
28.1%
B 9
 
10.1%
C 8
 
9.0%
T 8
 
9.0%
Z 7
 
7.9%
N 7
 
7.9%
J 6
 
6.7%
Y 4
 
4.5%
I 3
 
3.4%
H 3
 
3.4%
Other values (6) 9
 
10.1%
Space Separator
ValueCountFrequency (%)
37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360
89.8%
Common 41
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 44
 
12.2%
n 33
 
9.2%
a 29
 
8.1%
S 25
 
6.9%
e 22
 
6.1%
g 20
 
5.6%
h 18
 
5.0%
u 18
 
5.0%
s 17
 
4.7%
o 13
 
3.6%
Other values (27) 121
33.6%
Common
ValueCountFrequency (%)
37
90.2%
( 2
 
4.9%
) 2
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 44
 
11.0%
37
 
9.2%
n 33
 
8.2%
a 29
 
7.2%
S 25
 
6.2%
e 22
 
5.5%
g 20
 
5.0%
h 18
 
4.5%
u 18
 
4.5%
s 17
 
4.2%
Other values (30) 138
34.4%

BULD_YR
Real number (ℝ)

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.449
Minimum1997
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:55.644204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2003
Q12011
median2013
Q32014
95-th percentile2015
Maximum2016
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.153619
Coefficient of variation (CV)0.0020649885
Kurtosis4.0630796
Mean2011.449
Median Absolute Deviation (MAD)1
Skewness-2.0203821
Sum98561
Variance17.252551
MonotonicityNot monotonic
2023-12-10T23:25:55.769020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2013 14
28.6%
2014 11
22.4%
2011 5
 
10.2%
2012 3
 
6.1%
2010 3
 
6.1%
2015 3
 
6.1%
2003 2
 
4.1%
2016 2
 
4.1%
1998 1
 
2.0%
2007 1
 
2.0%
Other values (4) 4
 
8.2%
ValueCountFrequency (%)
1997 1
 
2.0%
1998 1
 
2.0%
2003 2
 
4.1%
2005 1
 
2.0%
2006 1
 
2.0%
2007 1
 
2.0%
2008 1
 
2.0%
2010 3
6.1%
2011 5
10.2%
2012 3
6.1%
ValueCountFrequency (%)
2016 2
 
4.1%
2015 3
 
6.1%
2014 11
22.4%
2013 14
28.6%
2012 3
 
6.1%
2011 5
 
10.2%
2010 3
 
6.1%
2008 1
 
2.0%
2007 1
 
2.0%
2006 1
 
2.0%

DDWGHT
Real number (ℝ)

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91761.306
Minimum32385
Maximum206097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:55.905338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32385
5-th percentile37500
Q156520
median72873
Q3114563
95-th percentile181928.2
Maximum206097
Range173712
Interquartile range (IQR)58043

Descriptive statistics

Standard deviation56089.595
Coefficient of variation (CV)0.61125541
Kurtosis-0.69779026
Mean91761.306
Median Absolute Deviation (MAD)32392
Skewness0.92709534
Sum4496304
Variance3.1460427 × 109
MonotonicityNot monotonic
2023-12-10T23:25:56.034910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
40481 4
 
8.2%
37500 3
 
6.1%
58000 2
 
4.1%
179816 2
 
4.1%
81386 1
 
2.0%
35313 1
 
2.0%
56678 1
 
2.0%
63478 1
 
2.0%
56520 1
 
2.0%
61398 1
 
2.0%
Other values (32) 32
65.3%
ValueCountFrequency (%)
32385 1
 
2.0%
35313 1
 
2.0%
37500 3
6.1%
37666 1
 
2.0%
38980 1
 
2.0%
39017 1
 
2.0%
40481 4
8.2%
56520 1
 
2.0%
56526 1
 
2.0%
56678 1
 
2.0%
ValueCountFrequency (%)
206097 1
2.0%
206037 1
2.0%
182307 1
2.0%
181360 1
2.0%
181031 1
2.0%
180000 1
2.0%
179816 2
4.1%
179667 1
2.0%
179549 1
2.0%
176247 1
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:12
Maximum2022-01-15 13:24:28
2023-12-10T23:25:56.170559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:56.359511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-05-24 07:56:36
Maximum2022-07-17 22:00:08
2023-12-10T23:25:56.496135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:56.639206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.412903
Minimum-36.190601
Maximum59.889
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)34.7%
Memory size573.0 B
2023-12-10T23:25:56.792159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-36.190601
5-th percentile-30.556461
Q1-9.4755
median12.4967
Q331.6064
95-th percentile46.679821
Maximum59.889
Range96.079601
Interquartile range (IQR)41.0819

Descriptive statistics

Standard deviation25.200318
Coefficient of variation (CV)2.2080551
Kurtosis-0.93503306
Mean11.412903
Median Absolute Deviation (MAD)19.91457
Skewness-0.22764676
Sum559.23223
Variance635.05605
MonotonicityNot monotonic
2023-12-10T23:25:56.949566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
15.1533 1
 
2.0%
-20.309799 1
 
2.0%
19.3081 1
 
2.0%
10.1283 1
 
2.0%
-21.6 1
 
2.0%
18.0217 1
 
2.0%
11.2617 1
 
2.0%
24.926701 1
 
2.0%
-25.1632 1
 
2.0%
32.318298 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-36.190601 1
2.0%
-34.741501 1
2.0%
-33.926701 1
2.0%
-25.501101 1
2.0%
-25.1632 1
2.0%
-24.092199 1
2.0%
-21.6 1
2.0%
-20.385799 1
2.0%
-20.309799 1
2.0%
-10.0801 1
2.0%
ValueCountFrequency (%)
59.889 1
2.0%
50.9963 1
2.0%
46.937901 1
2.0%
46.292702 1
2.0%
40.1483 1
2.0%
38.937302 1
2.0%
37.4659 1
2.0%
37.32 1
2.0%
36.284401 1
2.0%
36.248299 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.413116
Minimum-129.34199
Maximum166.248
Zeros0
Zeros (%)0.0%
Negative14
Negative (%)28.6%
Memory size573.0 B
2023-12-10T23:25:57.100451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-129.34199
5-th percentile-94.648077
Q1-3.35435
median52.959599
Q3115.138
95-th percentile134.559
Maximum166.248
Range295.59
Interquartile range (IQR)118.49235

Descriptive statistics

Standard deviation82.101372
Coefficient of variation (CV)1.9357543
Kurtosis-0.8959743
Mean42.413116
Median Absolute Deviation (MAD)62.178401
Skewness-0.54032983
Sum2078.2427
Variance6740.6353
MonotonicityNot monotonic
2023-12-10T23:25:57.236754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-129.341995 1
 
2.0%
118.57 1
 
2.0%
90.457603 1
 
2.0%
87.438301 1
 
2.0%
166.248001 1
 
2.0%
115.138 1
 
2.0%
127.517998 1
 
2.0%
54.540001 1
 
2.0%
160.514008 1
 
2.0%
119.727997 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-129.341995 1
2.0%
-124.991997 1
2.0%
-97.757797 1
2.0%
-89.983498 1
2.0%
-81.235603 1
2.0%
-78.187698 1
2.0%
-76.418404 1
2.0%
-61.618 1
2.0%
-57.805801 1
2.0%
-48.7533 1
2.0%
ValueCountFrequency (%)
166.248001 1
2.0%
160.514008 1
2.0%
139.253006 1
2.0%
127.517998 1
2.0%
122.036003 1
2.0%
121.571999 1
2.0%
120.824997 1
2.0%
119.727997 1
2.0%
118.57 1
2.0%
116.57 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4535517
Minimum-35.049702
Maximum69.060699
Zeros0
Zeros (%)0.0%
Negative26
Negative (%)53.1%
Memory size573.0 B
2023-12-10T23:25:57.378285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-35.049702
5-th percentile-33.742319
Q1-21.178301
median-1.355
Q325.2253
95-th percentile51.082501
Maximum69.060699
Range104.1104
Interquartile range (IQR)46.403601

Descriptive statistics

Standard deviation28.293027
Coefficient of variation (CV)19.464755
Kurtosis-0.69535688
Mean1.4535517
Median Absolute Deviation (MAD)22.579999
Skewness0.56733541
Sum71.224033
Variance800.49539
MonotonicityNot monotonic
2023-12-10T23:25:57.533826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-21.178301 1
 
2.0%
3.75744 1
 
2.0%
31.1371 1
 
2.0%
3.37001 1
 
2.0%
-21.019699 1
 
2.0%
-2.50167 1
 
2.0%
21.536699 1
 
2.0%
-33.810799 1
 
2.0%
27.661699 1
 
2.0%
45.673801 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-35.049702 1
2.0%
-34.310799 1
2.0%
-33.810799 1
2.0%
-33.639599 1
2.0%
-32.696201 1
2.0%
-32.570702 1
2.0%
-28.962299 1
2.0%
-25.704901 1
2.0%
-24.3557 1
2.0%
-23.976101 1
2.0%
ValueCountFrequency (%)
69.060699 1
2.0%
59.893299 1
2.0%
54.688301 1
2.0%
45.673801 1
2.0%
41.5284 1
2.0%
36.9907 1
2.0%
33.123299 1
2.0%
32.711601 1
2.0%
32.614399 1
2.0%
31.1371 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.656699
Minimum-172.168
Maximum164.107
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)30.6%
Memory size573.0 B
2023-12-10T23:25:57.653509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-172.168
5-th percentile-67.300461
Q1-26.549101
median33.054699
Q3116.225
95-th percentile143.214
Maximum164.107
Range336.27499
Interquartile range (IQR)142.7741

Descriptive statistics

Standard deviation76.541962
Coefficient of variation (CV)1.8374466
Kurtosis-0.40792452
Mean41.656699
Median Absolute Deviation (MAD)71.806
Skewness-0.41534802
Sum2041.1783
Variance5858.672
MonotonicityNot monotonic
2023-12-10T23:25:57.791192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
149.348007 1
 
2.0%
89.683601 1
 
2.0%
29.795799 1
 
2.0%
76.242996 1
 
2.0%
164.106995 1
 
2.0%
151.278 1
 
2.0%
108.362 1
 
2.0%
18.131901 1
 
2.0%
124.961998 1
 
2.0%
-172.167999 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-172.167999 1
2.0%
-72.620399 1
2.0%
-71.685501 1
2.0%
-60.7229 1
2.0%
-56.041 1
2.0%
-51.970299 1
2.0%
-48.206799 1
2.0%
-46.313999 1
2.0%
-46.287102 1
2.0%
-38.751301 1
2.0%
ValueCountFrequency (%)
164.106995 1
2.0%
151.278 1
2.0%
149.348007 1
2.0%
134.013 1
2.0%
127.727997 1
2.0%
126.873001 1
2.0%
126.724998 1
2.0%
124.961998 1
2.0%
118.538002 1
2.0%
118.529999 1
2.0%

MAX_VE
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.010018
Minimum12.1788
Maximum16.8882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:58.150183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.1788
5-th percentile14.1198
Q114.3819
median15.0359
Q315.4655
95-th percentile16.1478
Maximum16.8882
Range4.7094
Interquartile range (IQR)1.0836

Descriptive statistics

Standard deviation0.78623441
Coefficient of variation (CV)0.052380643
Kurtosis2.4297195
Mean15.010018
Median Absolute Deviation (MAD)0.5434
Skewness-0.58369764
Sum735.4909
Variance0.61816455
MonotonicityNot monotonic
2023-12-10T23:25:58.294229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
14.3055 1
 
2.0%
14.055 1
 
2.0%
14.9236 1
 
2.0%
15.1034 1
 
2.0%
14.6929 1
 
2.0%
16.1674 1
 
2.0%
12.1788 1
 
2.0%
15.1363 1
 
2.0%
16.0546 1
 
2.0%
14.231 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
12.1788 1
2.0%
13.9897 1
2.0%
14.055 1
2.0%
14.217 1
2.0%
14.231 1
2.0%
14.2381 1
2.0%
14.2398 1
2.0%
14.2743 1
2.0%
14.2746 1
2.0%
14.2848 1
2.0%
ValueCountFrequency (%)
16.8882 1
2.0%
16.3106 1
2.0%
16.1674 1
2.0%
16.1184 1
2.0%
16.0546 1
2.0%
15.9236 1
2.0%
15.7437 1
2.0%
15.7412 1
2.0%
15.6812 1
2.0%
15.6628 1
2.0%

AVE_VE
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.280817
Minimum9.00484
Maximum12.2961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:58.426386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.00484
5-th percentile9.78315
Q111.1554
median11.3901
Q311.6195
95-th percentile12.059
Maximum12.2961
Range3.29126
Interquartile range (IQR)0.4641

Descriptive statistics

Standard deviation0.68470305
Coefficient of variation (CV)0.06069623
Kurtosis3.9585911
Mean11.280817
Median Absolute Deviation (MAD)0.2347
Skewness-1.7703914
Sum552.76002
Variance0.46881826
MonotonicityNot monotonic
2023-12-10T23:25:58.565630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
11.1033 1
 
2.0%
11.2108 1
 
2.0%
10.8396 1
 
2.0%
11.918 1
 
2.0%
11.6027 1
 
2.0%
11.6195 1
 
2.0%
9.57005 1
 
2.0%
10.6822 1
 
2.0%
9.04543 1
 
2.0%
11.3148 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9.00484 1
2.0%
9.04543 1
2.0%
9.57005 1
2.0%
10.1028 1
2.0%
10.4379 1
2.0%
10.6822 1
2.0%
10.8396 1
2.0%
10.8683 1
2.0%
10.9753 1
2.0%
11.0355 1
2.0%
ValueCountFrequency (%)
12.2961 1
2.0%
12.2882 1
2.0%
12.1444 1
2.0%
11.9309 1
2.0%
11.918 1
2.0%
11.9089 1
2.0%
11.8874 1
2.0%
11.8157 1
2.0%
11.8104 1
2.0%
11.7616 1
2.0%

NVGTN_DIST
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51911480
Minimum26189200
Maximum82502900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:58.698659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26189200
5-th percentile29910580
Q144275200
median53382000
Q358969900
95-th percentile70743680
Maximum82502900
Range56313700
Interquartile range (IQR)14694700

Descriptive statistics

Standard deviation12109104
Coefficient of variation (CV)0.23326448
Kurtosis0.10635904
Mean51911480
Median Absolute Deviation (MAD)7155700
Skewness0.039547504
Sum2.5436625 × 109
Variance1.466304 × 1014
MonotonicityNot monotonic
2023-12-10T23:25:58.837176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
64730900 1
 
2.0%
82502900 1
 
2.0%
47182900 1
 
2.0%
60332500 1
 
2.0%
55401100 1
 
2.0%
55173700 1
 
2.0%
51180600 1
 
2.0%
54041300 1
 
2.0%
38192500 1
 
2.0%
51009700 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
26189200 1
2.0%
28692500 1
2.0%
28699100 1
2.0%
31727800 1
2.0%
37080000 1
2.0%
38066800 1
2.0%
38192500 1
2.0%
39646800 1
2.0%
40663900 1
2.0%
41173400 1
2.0%
ValueCountFrequency (%)
82502900 1
2.0%
76053000 1
2.0%
73182600 1
2.0%
67085300 1
2.0%
65988700 1
2.0%
65504500 1
2.0%
64730900 1
2.0%
63960300 1
2.0%
61985100 1
2.0%
60593900 1
2.0%

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
2023-12-10T23:25:59.012185image/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
2023-12-10T23:25:59.166640image/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_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOMAX_VEAVE_VENVGTN_DISTRN
02292850009633410MykonosBulk Carrier32.26225.520.05Sea Traders30.0New Century SB20138138601-Jan-2022 00:11:5017-Jul-2022 16:18:4515.1533-129.341995-21.178301149.34800714.305511.1033647309002
12292860009464651MiniBulk Carrier43.0248.020.2Sea Traders30.0New Century SB201211456301-Jan-2022 00:04:0117-Jul-2022 20:41:1450.99631.32422-20.745001116.22499815.743711.5302588485003
22292870009585601DelosBulk Carrier45.0282.224.75Sea Traders17.49New Times SB201217512515-Jan-2022 13:24:2817-Jul-2022 21:29:073.81615105.745003-14.5706117.07800314.5859.00484567976004
32292930009471630Minoan PioneerBulk Carrier38.0222.020.7Modion Maritime SA30.0Jiangsu New YZJ20119328301-Jan-2022 06:51:1505-Jun-2022 23:19:3529.862249.20019912.520247.18109915.618311.6586429301005
42293050009630664FijiBulk Carrier32.26225.520.05Sea Traders30.0New Century SB20138128501-Jan-2022 00:01:1117-Jul-2022 22:00:08-36.19060152.959599-5.99579.55000315.248311.2391760530006
52293080009609469LBC GreenChip Carrier36.5205.017.7NYK Blkshp Atlnt30.0Oshima Shipbuilding20137066301-Jan-2022 07:18:1111-Jul-2022 14:49:46-7.18513112.6859975.998394.43890414.381911.5247731826007
62293420009598799Iolcos ConfidenceBulk Carrier32.26217.019.7Iolcos Hellenic30.0Hudong Zhonghua20137603601-Jan-2022 00:04:2617-Jul-2022 15:53:5229.7068-89.98349825.225360.59569916.118411.5513655045008
72293470009512331NBA MagritteBulk Carrier32.26222.020.05NYK Blkshp Atlnt30.0Tsuneishi Zosen20138209901-Jan-2022 00:56:3317-Jul-2022 21:28:3436.248299-2.415-11.6751-35.89530215.662812.2961670853009
82293540009457854LevanteBulk Carrier38.0222.020.7Walshford Com30.0Jiangsu New YZJ20129320701-Jan-2022 09:12:0505-Jun-2022 13:59:12-3.7475114.434998-35.049702-56.04115.465511.39012618920010
92293610009662409SchinousaBulk Carrier45.0282.024.8Minerva Marine17.5141SCS Shipbuilding201417624701-Jan-2022 00:07:2717-Jul-2022 12:57:06-9.4755115.72000141.528431.905315.923611.34925896990011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOMAX_VEAVE_VENVGTN_DISTRN
392295850009750971NaomiBulk Carrier45.0286.424.9Ciner Denizcilik17.6168Shanghai Waigaoqiao201618103101-Jan-2022 00:32:4005-Jun-2022 23:50:2740.1483121.571999-17.240499117.77500216.310611.81045793490041
402295870009718686VulcaniaBulk Carrier32.26225.320.0Augustea Ship Mgmt30.0Jiangsu New YZJ20158203601-Jan-2022 00:26:2005-Jun-2022 23:42:1818.9321139.25300628.631701134.01315.445110.86834427520042
412296020009576961KIRAN AUSTRALIABULK CARRIER32.0194.518.5<NA>8.0<NA>20116351701-Jan-2022 02:40:0015-Jul-2022 15:43:541.46892103.827003-23.934999-46.31399915.464911.93093708000043
422296030009576973KIRAN ISTANBULBULK CARRIER32.0194.518.5<NA>8.0<NA>20136361005-Jan-2022 15:11:2017-Jul-2022 21:45:019.4845101.933998-21.777712.378315.4112.28826198510044
432296040009303144SeawindBulk Carrier32.26217.019.3Signal Maritime30.0Sanoyas20067563701-Jan-2022 03:06:1017-Jul-2022 21:54:2922.73329969.713303-1.355126.87300113.989711.58444965610045
442296090009286592ThalassiniBulk Carrier32.26222.019.9Astra Shipmgmt30.0Tsuneishi Zosen20058297701-Jan-2022 00:58:4617-Jul-2022 21:55:2233.19530.391701-18.190541.19720114.869511.45985964160046
452296110009674385POLA MUROMBULK CARRIER30.0176.7514.7<NA>29.4548<NA>20143750001-Jan-2022 00:02:2724-May-2022 07:56:36-25.501101-48.4374012.7259.8583315.362311.49392869910047
462296120009674397POLA PALEKHBULK CARRIER30.0176.7514.7<NA>29.4548<NA>20143750001-Jan-2022 00:01:5731-May-2022 03:51:30-24.092199-46.218359.89329930.21330116.888211.76163172780048
472296130009659787POLA INDIANBULK CARRIER30.0176.7514.7<NA>9.0<NA>20143766601-Jan-2022 00:09:1017-Jul-2022 21:48:2938.937302-76.41840469.06069933.05469915.357511.90895774720049
482296140009674373POLA UGLICHBULK CARRIER30.0176.7514.7<NA>29.4548<NA>20143750001-Jan-2022 01:20:1129-May-2022 08:49:1559.88930.211854.68830120.203315.012411.60224066390050