Overview

Dataset statistics

Number of variables24
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory212.7 B

Variable types

Numeric17
Text1
Categorical4
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001316

Alerts

RL_POWER has constant value ""Constant
SHIP_KIND is highly imbalanced (85.6%)Imbalance
MMSI has unique valuesUnique
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM has unique valuesUnique
DPTRP_LA has unique valuesUnique
DPTRP_LO has unique valuesUnique
DTNT_LA has unique valuesUnique
DTNT_LO has unique valuesUnique
ADDTI_RSTC has unique valuesUnique
TOT_RSTC has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:25:07.759619
Analysis finished2023-12-10 14:25:08.048993
Duration0.29 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%
Mean5.4249725 × 108
Minimum5.4230533 × 108
Maximum5.4294533 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:08.119812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.4230533 × 108
5-th percentile5.4231573 × 108
Q15.4238933 × 108
median5.4244833 × 108
Q35.4255633 × 108
95-th percentile5.4280913 × 108
Maximum5.4294533 × 108
Range640000
Interquartile range (IQR)167000

Descriptive statistics

Standard deviation152166.28
Coefficient of variation (CV)0.00028049226
Kurtosis1.550103
Mean5.4249725 × 108
Median Absolute Deviation (MAD)89000
Skewness1.2703673
Sum2.6582365 × 1010
Variance2.3154577 × 1010
MonotonicityNot monotonic
2023-12-10T23:25:08.265272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
542367333 1
 
2.0%
542552333 1
 
2.0%
542429333 1
 
2.0%
542421333 1
 
2.0%
542422333 1
 
2.0%
542537333 1
 
2.0%
542543333 1
 
2.0%
542548333 1
 
2.0%
542549333 1
 
2.0%
542556333 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
542305333 1
2.0%
542308333 1
2.0%
542313333 1
2.0%
542319333 1
2.0%
542328333 1
2.0%
542329333 1
2.0%
542361333 1
2.0%
542362333 1
2.0%
542367333 1
2.0%
542369333 1
2.0%
ValueCountFrequency (%)
542945333 1
2.0%
542932333 1
2.0%
542814333 1
2.0%
542801333 1
2.0%
542703333 1
2.0%
542697333 1
2.0%
542657333 1
2.0%
542637333 1
2.0%
542582333 1
2.0%
542581333 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2589954.1
Minimum2001866
Maximum2965438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:08.477174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001866
5-th percentile2008033.6
Q12433588
median2689462
Q32789092
95-th percentile2955581.2
Maximum2965438
Range963572
Interquartile range (IQR)355504

Descriptive statistics

Standard deviation351105.05
Coefficient of variation (CV)0.1355642
Kurtosis-0.8779603
Mean2589954.1
Median Absolute Deviation (MAD)232988
Skewness-0.81496846
Sum1.2690775 × 108
Variance1.2327476 × 1011
MonotonicityNot monotonic
2023-12-10T23:25:08.614569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2013778 1
 
2.0%
2685349 1
 
2.0%
2646240 1
 
2.0%
2781301 1
 
2.0%
2781363 1
 
2.0%
2646252 1
 
2.0%
2789016 1
 
2.0%
2646264 1
 
2.0%
2646238 1
 
2.0%
2689450 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2001866 1
2.0%
2001878 1
2.0%
2008008 1
2.0%
2008072 1
2.0%
2008084 1
2.0%
2008096 1
2.0%
2013704 1
2.0%
2013716 1
2.0%
2013778 1
2.0%
2013780 1
2.0%
ValueCountFrequency (%)
2965438 1
2.0%
2965397 1
2.0%
2955586 1
2.0%
2955574 1
2.0%
2955562 1
2.0%
2955550 1
2.0%
2955548 1
2.0%
2955536 1
2.0%
2942228 1
2.0%
2941494 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:25:08.838759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length12.44898
Min length9

Characters and Unicode

Total characters610
Distinct characters41
Distinct categories2 ?
Distinct scripts1 ?
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 rowPdqfkhvwhuPdhuvn
2nd rowPxufldPdhuvn
3rd rowPdqlodPdhuvn
4th rowPxpedlPdhuvn
5th rowPddvwulfkwPdhuvn
ValueCountFrequency (%)
pdqfkhvwhupdhuvn 1
 
2.0%
pdhuvnhyrud 1
 
2.0%
pdhuvnedowlpruh 1
 
2.0%
pdhuvnhoed 1
 
2.0%
pdhuvnhgprqwrq 1
 
2.0%
pdhuvnehqwrqylooh 1
 
2.0%
pdhuvnhvvhq 1
 
2.0%
pdhuvneurrnobq 1
 
2.0%
pdhuvnervwrq 1
 
2.0%
pdhuvnqhzsruw 1
 
2.0%
Other values (39) 39
79.6%
2023-12-10T23:25:09.180746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 96
15.7%
h 59
 
9.7%
u 52
 
8.5%
v 46
 
7.5%
P 41
 
6.7%
q 36
 
5.9%
n 36
 
5.9%
r 27
 
4.4%
w 22
 
3.6%
l 18
 
3.0%
Other values (31) 177
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 483
79.2%
Uppercase Letter 127
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 96
19.9%
h 59
12.2%
u 52
10.8%
v 46
9.5%
q 36
 
7.5%
n 36
 
7.5%
r 27
 
5.6%
w 22
 
4.6%
l 18
 
3.7%
o 17
 
3.5%
Other values (14) 74
15.3%
Uppercase Letter
ValueCountFrequency (%)
P 41
32.3%
V 17
13.4%
F 14
 
11.0%
D 11
 
8.7%
U 8
 
6.3%
H 8
 
6.3%
Y 6
 
4.7%
Q 6
 
4.7%
E 4
 
3.1%
W 3
 
2.4%
Other values (7) 9
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 610
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 96
15.7%
h 59
 
9.7%
u 52
 
8.5%
v 46
 
7.5%
P 41
 
6.7%
q 36
 
5.9%
n 36
 
5.9%
r 27
 
4.4%
w 22
 
3.6%
l 18
 
3.0%
Other values (31) 177
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 96
15.7%
h 59
 
9.7%
u 52
 
8.5%
v 46
 
7.5%
P 41
 
6.7%
q 36
 
5.9%
n 36
 
5.9%
r 27
 
4.4%
w 22
 
3.6%
l 18
 
3.0%
Other values (31) 177
29.0%

SHIP_KIND
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Container
48 
Cargo or Containership
 
1

Length

Max length22
Median length9
Mean length9.2653061
Min length9

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
Container 48
98.0%
Cargo or Containership 1
 
2.0%

Length

2023-12-10T23:25:09.317626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:25:09.406199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
container 48
94.1%
cargo 1
 
2.0%
or 1
 
2.0%
containership 1
 
2.0%

SHIP_WDTH
Real number (ℝ)

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.614286
Minimum15.6
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:09.489133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.6
5-th percentile27.1
Q135.2
median42.8
Q348.2
95-th percentile58.84
Maximum59
Range43.4
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.586568
Coefficient of variation (CV)0.24842767
Kurtosis-0.32849774
Mean42.614286
Median Absolute Deviation (MAD)7.6
Skewness-0.22344358
Sum2088.1
Variance112.07542
MonotonicityNot monotonic
2023-12-10T23:25:09.598524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
48.2 12
24.5%
42.8 8
16.3%
35.2 6
12.2%
58.6 5
10.2%
32.3 5
10.2%
59.0 3
 
6.1%
37.4 2
 
4.1%
29.8 2
 
4.1%
48.4 2
 
4.1%
31.0 1
 
2.0%
Other values (3) 3
 
6.1%
ValueCountFrequency (%)
15.6 1
 
2.0%
21.5 1
 
2.0%
25.3 1
 
2.0%
29.8 2
 
4.1%
31.0 1
 
2.0%
32.3 5
10.2%
35.2 6
12.2%
37.4 2
 
4.1%
42.8 8
16.3%
48.2 12
24.5%
ValueCountFrequency (%)
59.0 3
 
6.1%
58.6 5
10.2%
48.4 2
 
4.1%
48.2 12
24.5%
42.8 8
16.3%
37.4 2
 
4.1%
35.2 6
12.2%
32.3 5
10.2%
31.0 1
 
2.0%
29.8 2
 
4.1%

SHIP_LNTH
Real number (ℝ)

Distinct17
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.91837
Minimum74.2
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:09.717261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74.2
5-th percentile157.96
Q1235
median313.5
Q3350
95-th percentile378
Maximum378
Range303.8
Interquartile range (IQR)115

Descriptive statistics

Standard deviation76.734593
Coefficient of variation (CV)0.26837938
Kurtosis0.027596292
Mean285.91837
Median Absolute Deviation (MAD)36.5
Skewness-0.80761626
Sum14010
Variance5888.1978
MonotonicityNot monotonic
2023-12-10T23:25:09.829379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
350.0 7
14.3%
196.0 6
12.2%
313.5 6
12.2%
378.0 5
10.2%
278.2 5
10.2%
286.8 3
6.1%
376.2 3
6.1%
319.0 3
6.1%
198.7 2
 
4.1%
235.0 2
 
4.1%
Other values (7) 7
14.3%
ValueCountFrequency (%)
74.2 1
 
2.0%
108.7 1
 
2.0%
145.8 1
 
2.0%
176.2 1
 
2.0%
196.0 6
12.2%
198.7 2
 
4.1%
235.0 2
 
4.1%
278.2 5
10.2%
285.2 1
 
2.0%
286.8 3
6.1%
ValueCountFrequency (%)
378.0 5
10.2%
376.2 3
6.1%
350.0 7
14.3%
331.5 1
 
2.0%
319.0 3
6.1%
313.5 6
12.2%
287.0 1
 
2.0%
286.8 3
6.1%
285.2 1
 
2.0%
278.2 5
10.2%

SHIP_HGHT
Real number (ℝ)

Distinct18
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.132653
Minimum6.2
Maximum33.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:09.936313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile16.34
Q119.3
median24.6
Q329.9
95-th percentile33.2
Maximum33.2
Range27
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation6.2877324
Coefficient of variation (CV)0.26054874
Kurtosis0.32221691
Mean24.132653
Median Absolute Deviation (MAD)5.3
Skewness-0.62447912
Sum1182.5
Variance39.535578
MonotonicityNot monotonic
2023-12-10T23:25:10.054630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
18.1 6
12.2%
26.8 6
12.2%
33.2 5
10.2%
29.9 5
10.2%
21.4 5
10.2%
24.5 3
 
6.1%
30.3 3
 
6.1%
24.6 3
 
6.1%
29.8 2
 
4.1%
16.4 2
 
4.1%
Other values (8) 9
18.4%
ValueCountFrequency (%)
6.2 1
 
2.0%
8.4 1
 
2.0%
16.3 1
 
2.0%
16.4 2
 
4.1%
17.4 1
 
2.0%
18.1 6
12.2%
19.3 2
 
4.1%
21.4 5
10.2%
24.1 1
 
2.0%
24.2 1
 
2.0%
ValueCountFrequency (%)
33.2 5
10.2%
30.3 3
6.1%
29.9 5
10.2%
29.8 2
 
4.1%
26.8 6
12.2%
24.8 1
 
2.0%
24.6 3
6.1%
24.5 3
6.1%
24.2 1
 
2.0%
24.1 1
 
2.0%

SHIP_OWNER_NM
Categorical

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
Pdhuvn
21 
KdpexujVxg
10 
3
5 
QdyljduhFdslwdo
3 
ErFrpOhdvlqj
3 
Other values (4)
7 

Length

Max length17
Median length15
Mean length7.9795918
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
Pdhuvn 21
42.9%
KdpexujVxg 10
20.4%
3 5
 
10.2%
QdyljduhFdslwdo 3
 
6.1%
ErFrpOhdvlqj 3
 
6.1%
PlqvkhqjIlqdqfldo 2
 
4.1%
PdhuvnFrpsdqb 2
 
4.1%
QD 2
 
4.1%
UrbdoDufwlfOlqh 1
 
2.0%

Length

2023-12-10T23:25:10.202139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:25:10.402212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pdhuvn 21
42.9%
kdpexujvxg 10
20.4%
3 5
 
10.2%
qdyljduhfdslwdo 3
 
6.1%
erfrpohdvlqj 3
 
6.1%
plqvkhqjilqdqfldo 2
 
4.1%
pdhuvnfrpsdqb 2
 
4.1%
qd 2
 
4.1%
urbdodufwlfolqh 1
 
2.0%

DRAFT
Real number (ℝ)

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.857143
Minimum4.5
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:10.518986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile11.2
Q112.5
median14
Q316
95-th percentile16.8
Maximum17
Range12.5
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.5136295
Coefficient of variation (CV)0.18139594
Kurtosis3.7227716
Mean13.857143
Median Absolute Deviation (MAD)2
Skewness-1.4868509
Sum679
Variance6.3183333
MonotonicityNot monotonic
2023-12-10T23:25:10.654053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12.0 7
14.3%
16.0 7
14.3%
14.0 6
12.2%
16.5 5
10.2%
14.5 5
10.2%
12.5 5
10.2%
15.0 4
8.2%
11.2 3
6.1%
17.0 3
6.1%
13.0 2
 
4.1%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
4.5 1
 
2.0%
6.4 1
 
2.0%
11.2 3
6.1%
12.0 7
14.3%
12.5 5
10.2%
13.0 2
 
4.1%
14.0 6
12.2%
14.5 5
10.2%
15.0 4
8.2%
16.0 7
14.3%
ValueCountFrequency (%)
17.0 3
6.1%
16.5 5
10.2%
16.0 7
14.3%
15.0 4
8.2%
14.5 5
10.2%
14.0 6
12.2%
13.0 2
 
4.1%
12.5 5
10.2%
12.0 7
14.3%
11.2 3
6.1%

SHPYRD_NM
Categorical

Distinct10
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
KbxqgdlKLXovdq
14 
GdhzrrGVPH
12 
Yronvzhuiw
7 
FRVFRKLCkrxvkdq
6 
KbxqgdlVdpkrKL
3 
Other values (5)
7 

Length

Max length15
Median length14
Mean length11.77551
Min length2

Unique

Unique3 ?
Unique (%)6.1%

Sample

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

Common Values

ValueCountFrequency (%)
KbxqgdlKLXovdq 14
28.6%
GdhzrrGVPH 12
24.5%
Yronvzhuiw 7
14.3%
FRVFRKLCkrxvkdq 6
12.2%
KbxqgdlVdpkrKL 3
 
6.1%
VdpvxqjKL 2
 
4.1%
QD 2
 
4.1%
KxdqjsxZhqfkrqj 1
 
2.0%
RghqvhOlqgr 1
 
2.0%
FVEFNhhoxqj 1
 
2.0%

Length

2023-12-10T23:25:10.808645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:25:10.937529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kbxqgdlklxovdq 14
28.6%
gdhzrrgvph 12
24.5%
yronvzhuiw 7
14.3%
frvfrklckrxvkdq 6
12.2%
kbxqgdlvdpkrkl 3
 
6.1%
vdpvxqjkl 2
 
4.1%
qd 2
 
4.1%
kxdqjsxzhqfkrqj 1
 
2.0%
rghqvholqgr 1
 
2.0%
fvefnhhoxqj 1
 
2.0%

BULD_YR
Real number (ℝ)

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.8367
Minimum1994
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:11.081314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile2001.2
Q12008
median2012
Q32018
95-th percentile2019
Maximum2020
Range26
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7130951
Coefficient of variation (CV)0.0028397409
Kurtosis1.4279036
Mean2011.8367
Median Absolute Deviation (MAD)4
Skewness-0.92907546
Sum98580
Variance32.639456
MonotonicityNot monotonic
2023-12-10T23:25:11.552551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2018 8
16.3%
2011 7
14.3%
2014 6
12.2%
2008 5
10.2%
2010 4
8.2%
2013 4
8.2%
2019 3
 
6.1%
2006 3
 
6.1%
2007 2
 
4.1%
2012 2
 
4.1%
Other values (3) 5
10.2%
ValueCountFrequency (%)
1994 1
 
2.0%
1998 2
 
4.1%
2006 3
6.1%
2007 2
 
4.1%
2008 5
10.2%
2010 4
8.2%
2011 7
14.3%
2012 2
 
4.1%
2013 4
8.2%
2014 6
12.2%
ValueCountFrequency (%)
2020 2
 
4.1%
2019 3
 
6.1%
2018 8
16.3%
2014 6
12.2%
2013 4
8.2%
2012 2
 
4.1%
2011 7
14.3%
2010 4
8.2%
2008 5
10.2%
2007 2
 
4.1%

DDWGHT
Real number (ℝ)

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103030.35
Minimum2700
Maximum213971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:11.679718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2700
5-th percentile19627.2
Q153701
median107978
Q3146046
95-th percentile204513
Maximum213971
Range211271
Interquartile range (IQR)92345

Descriptive statistics

Standard deviation60329.793
Coefficient of variation (CV)0.58555362
Kurtosis-0.99434727
Mean103030.35
Median Absolute Deviation (MAD)54277
Skewness0.21582133
Sum5048487
Variance3.6396839 × 109
MonotonicityNot monotonic
2023-12-10T23:25:11.825119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
124458 6
12.2%
40000 6
12.2%
190326 5
10.2%
146046 5
10.2%
53701 5
10.2%
93551 3
 
6.1%
213971 3
 
6.1%
107978 3
 
6.1%
61983 2
 
4.1%
153514 2
 
4.1%
Other values (9) 9
18.4%
ValueCountFrequency (%)
2700 1
 
2.0%
5817 1
 
2.0%
15600 1
 
2.0%
25668 1
 
2.0%
33796 1
 
2.0%
34622 1
 
2.0%
40000 6
12.2%
53701 5
10.2%
61983 2
 
4.1%
93025 1
 
2.0%
ValueCountFrequency (%)
213971 3
6.1%
190326 5
10.2%
153514 2
 
4.1%
146046 5
10.2%
124458 6
12.2%
112271 1
 
2.0%
110381 1
 
2.0%
107978 3
6.1%
93551 3
6.1%
93025 1
 
2.0%

DPTR_HMS
Date

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 12:00:00
Maximum2023-01-02 12:00:00
2023-12-10T23:25:11.935897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:12.055321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

ARVL_HMS
Date

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-04-30 12:00:00
Maximum2023-04-30 18:00:00
2023-12-10T23:25:12.169399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:12.270849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.020909
Minimum-34.580399
Maximum64.167503
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)8.2%
Memory size573.0 B
2023-12-10T23:25:12.402164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-34.580399
5-th percentile-5.944526
Q119.857
median33.485298
Q345.8689
95-th percentile57.479958
Maximum64.167503
Range98.747902
Interquartile range (IQR)26.0119

Descriptive statistics

Standard deviation22.265686
Coefficient of variation (CV)0.7416726
Kurtosis0.8436145
Mean30.020909
Median Absolute Deviation (MAD)13.628298
Skewness-0.88352248
Sum1471.0245
Variance495.76076
MonotonicityNot monotonic
2023-12-10T23:25:12.575505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
36.013699 1
 
2.0%
19.857 1
 
2.0%
30.9231 1
 
2.0%
30.004101 1
 
2.0%
34.603298 1
 
2.0%
34.181499 1
 
2.0%
29.979601 1
 
2.0%
-3.69116 1
 
2.0%
24.7607 1
 
2.0%
41.114799 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-34.580399 1
2.0%
-29.5609 1
2.0%
-7.44677 1
2.0%
-3.69116 1
2.0%
3.05562 1
2.0%
4.01074 1
2.0%
4.56724 1
2.0%
5.57923 1
2.0%
5.71607 1
2.0%
5.71957 1
2.0%
ValueCountFrequency (%)
64.167503 1
2.0%
60.891201 1
2.0%
57.509998 1
2.0%
57.434898 1
2.0%
57.007801 1
2.0%
56.178699 1
2.0%
54.6679 1
2.0%
54.601002 1
2.0%
53.197701 1
2.0%
52.070702 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.437591
Minimum-167.047
Maximum176.035
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)32.7%
Memory size573.0 B
2023-12-10T23:25:12.705339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-167.047
5-th percentile-104.43824
Q1-11.6103
median14.5846
Q384.469398
95-th percentile129.78419
Maximum176.035
Range343.082
Interquartile range (IQR)96.079698

Descriptive statistics

Standard deviation79.977584
Coefficient of variation (CV)3.1440707
Kurtosis-0.11869085
Mean25.437591
Median Absolute Deviation (MAD)48.873899
Skewness-0.22303652
Sum1246.442
Variance6396.414
MonotonicityNot monotonic
2023-12-10T23:25:12.855822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
16.421301 1
 
2.0%
-116.238998 1
 
2.0%
130.436996 1
 
2.0%
-167.046997 1
 
2.0%
127.997002 1
 
2.0%
128.408005 1
 
2.0%
123.139999 1
 
2.0%
41.4622 1
 
2.0%
67.212196 1
 
2.0%
2.30232 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-167.046997 1
2.0%
-160.690994 1
2.0%
-116.238998 1
2.0%
-86.737099 1
2.0%
-80.375504 1
2.0%
-58.3671 1
2.0%
-56.5364 1
2.0%
-51.716301 1
2.0%
-46.075699 1
2.0%
-34.289299 1
2.0%
ValueCountFrequency (%)
176.035004 1
2.0%
162.990005 1
2.0%
130.436996 1
2.0%
128.804993 1
2.0%
128.408005 1
2.0%
127.997002 1
2.0%
123.57 1
2.0%
123.277 1
2.0%
123.139999 1
2.0%
122.751999 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.702548
Minimum-31.909599
Maximum66.141403
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)12.2%
Memory size573.0 B
2023-12-10T23:25:13.002244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-31.909599
5-th percentile-25.86728
Q120.188299
median35.780998
Q344.103699
95-th percentile58.214321
Maximum66.141403
Range98.051002
Interquartile range (IQR)23.9154

Descriptive statistics

Standard deviation25.554179
Coefficient of variation (CV)0.89031044
Kurtosis0.4948741
Mean28.702548
Median Absolute Deviation (MAD)13.981102
Skewness-1.0664634
Sum1406.4249
Variance653.01604
MonotonicityNot monotonic
2023-12-10T23:25:13.133321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
36.8424 1
 
2.0%
4.18372 1
 
2.0%
30.938999 1
 
2.0%
36.000401 1
 
2.0%
49.203201 1
 
2.0%
33.892899 1
 
2.0%
36.006699 1
 
2.0%
7.0485 1
 
2.0%
17.4841 1
 
2.0%
40.9617 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-31.909599 1
2.0%
-30.235399 1
2.0%
-26.413799 1
2.0%
-25.047501 1
2.0%
-23.9657 1
2.0%
-22.92 1
2.0%
4.18372 1
2.0%
5.48845 1
2.0%
7.0485 1
2.0%
10.2119 1
2.0%
ValueCountFrequency (%)
66.141403 1
2.0%
61.812099 1
2.0%
59.329201 1
2.0%
56.542 1
2.0%
55.5261 1
2.0%
54.928001 1
2.0%
54.3801 1
2.0%
53.705601 1
2.0%
53.381699 1
2.0%
51.337601 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.868969
Minimum-139.455
Maximum173.284
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)30.6%
Memory size573.0 B
2023-12-10T23:25:13.278236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-139.455
5-th percentile-92.644758
Q1-5.49895
median15.2042
Q399.248802
95-th percentile129.4582
Maximum173.284
Range312.739
Interquartile range (IQR)104.74775

Descriptive statistics

Standard deviation73.13465
Coefficient of variation (CV)2.5333308
Kurtosis-0.42262233
Mean28.868969
Median Absolute Deviation (MAD)56.607697
Skewness-0.117124
Sum1414.5795
Variance5348.677
MonotonicityNot monotonic
2023-12-10T23:25:13.431597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1.10423 1
 
2.0%
99.248802 1
 
2.0%
129.897003 1
 
2.0%
120.196999 1
 
2.0%
173.283997 1
 
2.0%
134.901993 1
 
2.0%
120.203003 1
 
2.0%
52.2995 1
 
2.0%
56.913502 1
 
2.0%
28.6726 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-139.455002 1
2.0%
-124.658997 1
2.0%
-106.583 1
2.0%
-71.737396 1
2.0%
-53.969601 1
2.0%
-49.721001 1
2.0%
-47.216499 1
2.0%
-46.291698 1
2.0%
-44.5215 1
2.0%
-43.144001 1
2.0%
ValueCountFrequency (%)
173.283997 1
2.0%
134.901993 1
2.0%
129.897003 1
2.0%
128.800003 1
2.0%
122.821999 1
2.0%
122.160004 1
2.0%
121.310997 1
2.0%
120.984001 1
2.0%
120.203003 1
2.0%
120.196999 1
2.0%

ADDTI_RSTC
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39473.401
Minimum3020.66
Maximum97067.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:13.590724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3020.66
5-th percentile9352.794
Q119864.7
median39724.1
Q354598.2
95-th percentile87221.56
Maximum97067.7
Range94047.04
Interquartile range (IQR)34733.5

Descriptive statistics

Standard deviation22942.786
Coefficient of variation (CV)0.58122143
Kurtosis0.0084589923
Mean39473.401
Median Absolute Deviation (MAD)15460.9
Skewness0.59419189
Sum1934196.6
Variance5.2637144 × 108
MonotonicityNot monotonic
2023-12-10T23:25:13.718143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
42145.1 1
 
2.0%
25775.9 1
 
2.0%
21124.4 1
 
2.0%
30148.1 1
 
2.0%
62610.9 1
 
2.0%
18654.8 1
 
2.0%
97067.7 1
 
2.0%
13895.3 1
 
2.0%
12985.4 1
 
2.0%
21387.8 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3020.66 1
2.0%
6305.27 1
2.0%
9257.77 1
2.0%
9495.33 1
2.0%
12777.4 1
2.0%
12985.4 1
2.0%
13279.7 1
2.0%
13895.3 1
2.0%
14392.1 1
2.0%
16931.9 1
2.0%
ValueCountFrequency (%)
97067.7 1
2.0%
90349.1 1
2.0%
89567.8 1
2.0%
83702.2 1
2.0%
69550.7 1
2.0%
62610.9 1
2.0%
60478.8 1
2.0%
57547.2 1
2.0%
55959.0 1
2.0%
55185.0 1
2.0%

TOT_RSTC
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171063.02
Minimum6737.18
Maximum393892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:13.869906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6737.18
5-th percentile26889.22
Q187347.2
median154180
Q3250452
95-th percentile348407.4
Maximum393892
Range387154.82
Interquartile range (IQR)163104.8

Descriptive statistics

Standard deviation103824.97
Coefficient of variation (CV)0.60693992
Kurtosis-0.76135796
Mean171063.02
Median Absolute Deviation (MAD)81280.2
Skewness0.3986932
Sum8382088
Variance1.0779625 × 1010
MonotonicityNot monotonic
2023-12-10T23:25:14.019635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
239312.0 1
 
2.0%
120282.0 1
 
2.0%
76213.9 1
 
2.0%
70000.7 1
 
2.0%
195078.0 1
 
2.0%
91374.5 1
 
2.0%
321858.0 1
 
2.0%
99030.0 1
 
2.0%
87347.2 1
 
2.0%
72899.8 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
6737.18 1
2.0%
13777.1 1
2.0%
24034.3 1
2.0%
31171.6 1
2.0%
42591.6 1
2.0%
49732.0 1
2.0%
53509.7 1
2.0%
55260.9 1
2.0%
68367.5 1
2.0%
70000.7 1
2.0%
ValueCountFrequency (%)
393892.0 1
2.0%
375403.0 1
2.0%
360307.0 1
2.0%
330558.0 1
2.0%
323961.0 1
2.0%
321858.0 1
2.0%
305496.0 1
2.0%
305013.0 1
2.0%
288475.0 1
2.0%
282518.0 1
2.0%

RL_POWER
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

2023-12-10T23:25:14.197313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:25:14.337455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

FUEL_CNSMP_QTY
Real number (ℝ)

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5920394 × 109
Minimum4.16408 × 108
Maximum9.55935 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:14.496813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.16408 × 108
5-th percentile1.178884 × 109
Q12.44405 × 109
median4.25582 × 109
Q36.29703 × 109
95-th percentile8.904678 × 109
Maximum9.55935 × 109
Range9.142942 × 109
Interquartile range (IQR)3.85298 × 109

Descriptive statistics

Standard deviation2.451355 × 109
Coefficient of variation (CV)0.53382708
Kurtosis-0.69052816
Mean4.5920394 × 109
Median Absolute Deviation (MAD)1.85517 × 109
Skewness0.35254211
Sum2.2500993 × 1011
Variance6.0091413 × 1018
MonotonicityNot monotonic
2023-12-10T23:25:14.696863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3931810000 2
 
4.1%
6020320000 1
 
2.0%
7823060000 1
 
2.0%
2460350000 1
 
2.0%
5583300000 1
 
2.0%
2444050000 1
 
2.0%
8665950000 1
 
2.0%
2694710000 1
 
2.0%
2400650000 1
 
2.0%
2246040000 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
416408000 1
2.0%
586884000 1
2.0%
940240000 1
2.0%
1536850000 1
2.0%
1562900000 1
2.0%
1764740000 1
2.0%
1843190000 1
2.0%
2054090000 1
2.0%
2146920000 1
2.0%
2246040000 1
2.0%
ValueCountFrequency (%)
9559350000 1
2.0%
9424340000 1
2.0%
8926830000 1
2.0%
8871450000 1
2.0%
8665950000 1
2.0%
8001820000 1
2.0%
7942070000 1
2.0%
7823060000 1
2.0%
7026830000 1
2.0%
6716150000 1
2.0%

CDBX
Real number (ℝ)

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.429962 × 1010
Minimum1.2967 × 109
Maximum2.97678 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:14.874859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2967 × 109
5-th percentile3.671046 × 109
Q17.61078 × 109
median1.32526 × 1010
Q31.96089 × 1010
95-th percentile2.772918 × 1010
Maximum2.97678 × 1010
Range2.84711 × 1010
Interquartile range (IQR)1.199812 × 1010

Descriptive statistics

Standard deviation7.6335168 × 109
Coefficient of variation (CV)0.53382656
Kurtosis-0.69052593
Mean1.429962 × 1010
Median Absolute Deviation (MAD)5.77698 × 109
Skewness0.35254253
Sum7.0068137 × 1011
Variance5.8270579 × 1019
MonotonicityNot monotonic
2023-12-10T23:25:15.055174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
12243700000 2
 
4.1%
18747300000 1
 
2.0%
24361000000 1
 
2.0%
7661530000 1
 
2.0%
17386400000 1
 
2.0%
7610780000 1
 
2.0%
26985800000 1
 
2.0%
8391310000 1
 
2.0%
7475620000 1
 
2.0%
6994180000 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
1296700000 1
2.0%
1827560000 1
2.0%
2927910000 1
2.0%
4785750000 1
2.0%
4866920000 1
2.0%
5495430000 1
2.0%
5739730000 1
2.0%
6396480000 1
2.0%
6685510000 1
2.0%
6994180000 1
2.0%
ValueCountFrequency (%)
29767800000 1
2.0%
29347400000 1
2.0%
27798100000 1
2.0%
27625800000 1
2.0%
26985800000 1
2.0%
24917700000 1
2.0%
24731600000 1
2.0%
24361000000 1
2.0%
21881500000 1
2.0%
20914100000 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
2023-12-10T23:25:15.211188image/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
2023-12-10T23:25:15.401282image/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_LOADDTI_RSTCTOT_RSTCRL_POWERFUEL_CNSMP_QTYCDBXRN
05423673332013778PdqfkhvwhuPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201819032601-Jan-2023 12:00:0030-Apr-2023 18:00:0036.01369916.42130136.84241.1042342145.1239312.006020320000187473000002
15423693332013780PxufldPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201819032601-Jan-2023 12:00:0030-Apr-2023 18:00:0035.076801128.80499336.23615.204254884.8393892.009559350000297678000003
25423613332013792PdqlodPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201819032601-Jan-2023 12:00:0030-Apr-2023 18:00:0030.4669122.7519995.4884587.03600324495.3163862.004255820000132526000004
35423623332013704PxpedlPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201819032601-Jan-2023 12:00:0030-Apr-2023 18:00:0030.101999123.5729.8062122.16000454598.2360307.008871450000276258000005
45423783332013716PddvwulfkwPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201919032601-Jan-2023 12:00:0030-Apr-2023 18:00:0057.50999811.393535.5853121.31099747267.3176655.005205290000162093000006
55423863332008072YrojdPdhuvnContainer35.2196.018.1Pdhuvn12.0FRVFRKLCkrxvkdq20184000001-Jan-2023 12:00:0030-Apr-2023 18:00:0054.667910.815751.3376014.2757513279.731171.60156290000048669200007
65423893332789092PdhuvnHpghqContainer48.2350.029.9PlqvkhqjIlqdqfldo16.0KbxqgdlKLXovdq201014604601-Jan-2023 12:00:0030-Apr-2023 18:00:0024.9088120.00900335.780998-139.45500255959.0152873.004694830000146197000008
75423053332777063VdqwdFdwdulqdContainer42.8286.824.5KdpexujVxg14.5GdhzrrGVPH20119355101-Jan-2023 12:00:0030-Apr-2023 18:00:005.7195781.7547-30.23539914.959639724.1172927.004868940000151619000009
85423083332777049VdqwdFodudContainer42.8286.824.5KdpexujVxg14.5GdhzrrGVPH20109355101-Jan-2023 12:00:0030-Apr-2023 18:00:00-29.560931.30590136.156898-5.4061531853.7131513.0045082900001403880000010
95423133332777075VdqwdFuxcContainer42.8286.824.5KdpexujVxg14.5GdhzrrGVPH20119355101-Jan-2023 12:00:0030-Apr-2023 18:00:00-7.44677-4.71615-31.90959929.45109942569.8198514.0053191600001656390000011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOADDTI_RSTCTOT_RSTCRL_POWERFUEL_CNSMP_QTYCDBXRN
395425813332770194UrphHasuhvvContainer48.4350.029.8QdyljduhFdslwdo16.0VdpvxqjKL201015351401-Jan-2023 12:00:0030-Apr-2023 18:00:0038.213902162.99000535.669701120.98400147845.8208227.0054352800001692550000041
405425823332770118VrxwkdpswrqHasuhvvContainer48.4350.029.8QdyljduhFdslwdo16.0VdpvxqjKL201115351401-Jan-2023 12:00:0030-Apr-2023 12:00:0027.049451.34780110.211971.81189734539.1221141.0049803200001550870000042
415426373332155198WxnxpdDufwlfdContainer31.0176.217.4UrbdoDufwlfOlqh12.0KxdqjsxZhqfkrqj20202566801-Jan-2023 12:00:0030-Apr-2023 18:00:0057.43489810.553359.329201-44.521560478.8133248.0037366100001163580000043
425426573332453198VdoobPdhuvnContainer42.8331.524.1Pdhuvn14.5RghqvhOlqgr199811038101-Jan-2023 12:00:0030-Apr-2023 18:00:0016.446699-160.69099420.188299-106.58383702.2375403.0094243400002934740000044
435426973332941494QDQRTDUFWLFDCargo or Containership15.674.26.2QD4.5QD2020270001-Jan-2023 12:00:0030-Apr-2023 18:00:0064.167503-51.71630161.812099-49.7210013020.666737.180416408000129670000045
445427033332497560DohadqghuPdhuvnContainer25.3145.816.3Pdhuvn11.2FVEFNhhoxqj19981560001-Jan-2023 12:00:0030-Apr-2023 18:00:0033.48529818.808532.77529915.26126305.2724034.30940240000292791000046
455428013332942228PdbylhzPdhuvnContainer59.0376.230.3Pdhuvn17.0GdhzrrGVPH201421397101-Jan-2023 12:00:0030-Apr-2023 12:00:005.5792384.46939824.285999118.22000142895.8190907.0051863600001615030000047
465428143332965397PhuhwhPdhuvnContainer59.0376.230.3Pdhuvn17.0GdhzrrGVPH201421397101-Jan-2023 12:00:0030-Apr-2023 18:00:0012.288648.79299953.7056018.3063848949.2250452.0070268300002188150000048
475429323332965438PruwhqPdhuvnContainer59.0376.230.3Pdhuvn17.0GdhzrrGVPH201421397101-Jan-2023 12:00:0030-Apr-2023 12:00:0034.27600122.730635.077301128.80000344475.8305013.0079420700002473160000049
485429453332433588LUHQDDUFWLFDContainer21.5108.78.4QD6.4QD1994581701-Jan-2023 12:00:0030-Apr-2023 18:00:0060.891201-46.07569966.141403-53.9696019257.7713777.10586884000182756000050