Overview

Dataset statistics

Number of variables22
Number of observations49
Missing cells98
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory196.7 B

Variable types

Numeric16
Text1
Categorical1
Unsupported2
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001096

Alerts

SHIP_OWNER_NM has 49 (100.0%) missing valuesMissing
SHPYRD_NM has 49 (100.0%) missing valuesMissing
MMSI 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
PRFMC has unique valuesUnique
FUEL_CNSMP_QTY has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique
SHIP_OWNER_NM is an unsupported type, check if it needs cleaning or further analysisUnsupported
SHPYRD_NM is an unsupported type, check if it needs cleaning or further analysisUnsupported
IMO_IDNTF_NO has 10 (20.4%) zerosZeros

Reproduction

Analysis started2023-12-10 14:42:48.313348
Analysis finished2023-12-10 14:42:48.540769
Duration0.23 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%
Mean4.3649334 × 108
Minimum2.48841 × 108
Maximum6.3601971 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:48.612780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.48841 × 108
5-th percentile2.5624682 × 108
Q14.1325769 × 108
median4.3100083 × 108
Q34.3160065 × 108
95-th percentile6.2681059 × 108
Maximum6.3601971 × 108
Range3.8717871 × 108
Interquartile range (IQR)18342964

Descriptive statistics

Standard deviation92733817
Coefficient of variation (CV)0.21245185
Kurtosis0.74508925
Mean4.3649334 × 108
Median Absolute Deviation (MAD)17743142
Skewness0.10229529
Sum2.1388174 × 1010
Variance8.5995609 × 1015
MonotonicityNot monotonic
2023-12-10T23:42:48.741653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
413328790 1
 
2.0%
413251550 1
 
2.0%
413434590 1
 
2.0%
636015172 1
 
2.0%
413246160 1
 
2.0%
636019547 1
 
2.0%
477815700 1
 
2.0%
636019710 1
 
2.0%
257061880 1
 
2.0%
431000497 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
248841000 1
2.0%
249827000 1
2.0%
255806026 1
2.0%
256908000 1
2.0%
257061880 1
2.0%
311000142 1
2.0%
412503190 1
2.0%
413221810 1
2.0%
413232230 1
2.0%
413233740 1
2.0%
ValueCountFrequency (%)
636019710 1
2.0%
636019547 1
2.0%
636015172 1
2.0%
613003714 1
2.0%
574003900 1
2.0%
538003834 1
2.0%
525109001 1
2.0%
525022093 1
2.0%
525015723 1
2.0%
525013025 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

ZEROS 

Distinct40
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7497139.4
Minimum0
Maximum9897664
Zeros10
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:48.865732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18726155
median9418200
Q39614804
95-th percentile9845271.8
Maximum9897664
Range9897664
Interquartile range (IQR)888649

Descriptive statistics

Standard deviation3849874.8
Coefficient of variation (CV)0.5135125
Kurtosis0.26655393
Mean7497139.4
Median Absolute Deviation (MAD)320947
Skewness-1.4877723
Sum3.6735983 × 108
Variance1.4821536 × 1013
MonotonicityNot monotonic
2023-12-10T23:42:48.993499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 10
 
20.4%
9614804 1
 
2.0%
9418200 1
 
2.0%
9604201 1
 
2.0%
9786384 1
 
2.0%
9145815 1
 
2.0%
9574729 1
 
2.0%
9397200 1
 
2.0%
9576313 1
 
2.0%
9539339 1
 
2.0%
Other values (30) 30
61.2%
ValueCountFrequency (%)
0 10
20.4%
8315554 1
 
2.0%
8703751 1
 
2.0%
8726155 1
 
2.0%
8741533 1
 
2.0%
9070101 1
 
2.0%
9070113 1
 
2.0%
9070125 1
 
2.0%
9115004 1
 
2.0%
9117375 1
 
2.0%
ValueCountFrequency (%)
9897664 1
2.0%
9865403 1
2.0%
9846483 1
2.0%
9843455 1
2.0%
9839234 1
2.0%
9786384 1
2.0%
9756054 1
2.0%
9739147 1
2.0%
9713325 1
2.0%
9690573 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:42:49.187437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length11.367347
Min length4

Characters and Unicode

Total characters557
Distinct characters36
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

Unique49 ?
Unique (%)100.0%

Sample

1st rowJIANG QUAN 6
2nd rowWARTA
3rd rowNEFTERUDOVOZ 50M
4th rowTERTNES
5th rowHENG HUI 58
ValueCountFrequency (%)
maru 11
 
9.7%
xin 3
 
2.7%
hai 3
 
2.7%
heng 2
 
1.8%
gao 2
 
1.8%
xing 2
 
1.8%
tong 2
 
1.8%
zhong 2
 
1.8%
6 2
 
1.8%
no.1 1
 
0.9%
Other values (83) 83
73.5%
2023-12-10T23:42:49.708277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64
 
11.5%
A 62
 
11.1%
I 46
 
8.3%
N 44
 
7.9%
O 35
 
6.3%
U 30
 
5.4%
R 28
 
5.0%
S 25
 
4.5%
G 23
 
4.1%
E 23
 
4.1%
Other values (26) 177
31.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 458
82.2%
Space Separator 64
 
11.5%
Decimal Number 31
 
5.6%
Other Punctuation 4
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 62
13.5%
I 46
 
10.0%
N 44
 
9.6%
O 35
 
7.6%
U 30
 
6.6%
R 28
 
6.1%
S 25
 
5.5%
G 23
 
5.0%
E 23
 
5.0%
M 22
 
4.8%
Other values (16) 120
26.2%
Decimal Number
ValueCountFrequency (%)
1 7
22.6%
8 5
16.1%
6 5
16.1%
9 4
12.9%
0 3
9.7%
2 3
9.7%
3 2
 
6.5%
5 2
 
6.5%
Space Separator
ValueCountFrequency (%)
64
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 458
82.2%
Common 99
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 62
13.5%
I 46
 
10.0%
N 44
 
9.6%
O 35
 
7.6%
U 30
 
6.6%
R 28
 
6.1%
S 25
 
5.5%
G 23
 
5.0%
E 23
 
5.0%
M 22
 
4.8%
Other values (16) 120
26.2%
Common
ValueCountFrequency (%)
64
64.6%
1 7
 
7.1%
8 5
 
5.1%
6 5
 
5.1%
9 4
 
4.0%
. 4
 
4.0%
0 3
 
3.0%
2 3
 
3.0%
3 2
 
2.0%
5 2
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64
 
11.5%
A 62
 
11.1%
I 46
 
8.3%
N 44
 
7.9%
O 35
 
6.3%
U 30
 
5.4%
R 28
 
5.0%
S 25
 
4.5%
G 23
 
4.1%
E 23
 
4.1%
Other values (26) 177
31.8%

SHIP_KIND
Categorical

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
BULK CARRIER
15 
Cement Carrier
11 
Cargo
10 
Chemical/Oil Product
6 
Bulk Carrier
4 
Other values (2)
3 

Length

Max length20
Median length18
Mean length12.163265
Min length5

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowBULK CARRIER
2nd rowBULK CARRIER
3rd rowOre or Oil Carrier
4th rowBULK CARRIER
5th rowChemical/Oil Product

Common Values

ValueCountFrequency (%)
BULK CARRIER 15
30.6%
Cement Carrier 11
22.4%
Cargo 10
20.4%
Chemical/Oil Product 6
 
12.2%
Bulk Carrier 4
 
8.2%
General Cargo 2
 
4.1%
Ore or Oil Carrier 1
 
2.0%

Length

2023-12-10T23:42:49.823743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:42:49.918089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
carrier 31
34.4%
bulk 19
21.1%
cargo 12
 
13.3%
cement 11
 
12.2%
chemical/oil 6
 
6.7%
product 6
 
6.7%
general 2
 
2.2%
ore 1
 
1.1%
or 1
 
1.1%
oil 1
 
1.1%

SHIP_WDTH
Real number (ℝ)

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.92449
Minimum9.6
Maximum32.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:50.014611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.6
5-th percentile12.4
Q116.8
median19.6
Q324.2
95-th percentile30.73
Maximum32.26
Range22.66
Interquartile range (IQR)7.4

Descriptive statistics

Standard deviation5.4505436
Coefficient of variation (CV)0.27356001
Kurtosis-0.04106187
Mean19.92449
Median Absolute Deviation (MAD)3.6
Skewness0.43827993
Sum976.3
Variance29.708425
MonotonicityNot monotonic
2023-12-10T23:42:50.106754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
21.0 8
16.3%
17.5 4
 
8.2%
24.4 3
 
6.1%
16.0 3
 
6.1%
18.0 3
 
6.1%
13.0 3
 
6.1%
32.26 2
 
4.1%
19.6 2
 
4.1%
24.6 1
 
2.0%
14.6 1
 
2.0%
Other values (19) 19
38.8%
ValueCountFrequency (%)
9.6 1
 
2.0%
10.2 1
 
2.0%
12.0 1
 
2.0%
13.0 3
6.1%
13.46 1
 
2.0%
14.0 1
 
2.0%
14.6 1
 
2.0%
16.0 3
6.1%
16.8 1
 
2.0%
17.4 1
 
2.0%
ValueCountFrequency (%)
32.26 2
4.1%
30.95 1
 
2.0%
30.4 1
 
2.0%
27.6 1
 
2.0%
26.2 1
 
2.0%
26.0 1
 
2.0%
24.6 1
 
2.0%
24.44 1
 
2.0%
24.4 3
6.1%
24.2 1
 
2.0%

SHIP_LNTH
Real number (ℝ)

Distinct40
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.82612
Minimum44.7
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:50.196443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44.7
5-th percentile61.4
Q1113.06
median119.95
Q3148.8
95-th percentile178.054
Maximum182
Range137.3
Interquartile range (IQR)35.74

Descriptive statistics

Standard deviation34.581485
Coefficient of variation (CV)0.27927455
Kurtosis-0.040585515
Mean123.82612
Median Absolute Deviation (MAD)23.05
Skewness-0.42823621
Sum6067.48
Variance1195.8791
MonotonicityNot monotonic
2023-12-10T23:42:50.295856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
134.0 4
 
8.2%
114.8 4
 
8.2%
149.8 3
 
6.1%
145.0 2
 
4.1%
44.99 1
 
2.0%
151.3 1
 
2.0%
115.33 1
 
2.0%
44.7 1
 
2.0%
105.0 1
 
2.0%
178.09 1
 
2.0%
Other values (30) 30
61.2%
ValueCountFrequency (%)
44.7 1
2.0%
44.99 1
2.0%
61.0 1
2.0%
62.0 1
2.0%
63.0 1
2.0%
71.0 1
2.0%
87.0 1
2.0%
95.32 1
2.0%
98.0 1
2.0%
99.93 1
2.0%
ValueCountFrequency (%)
182.0 1
 
2.0%
181.0 1
 
2.0%
178.09 1
 
2.0%
178.0 1
 
2.0%
177.0 1
 
2.0%
171.6 1
 
2.0%
159.6 1
 
2.0%
151.3 1
 
2.0%
149.8 3
6.1%
149.6 1
 
2.0%

SHIP_HGHT
Real number (ℝ)

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.479664
Minimum4.89849
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:50.403865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.89849
5-th percentile5.315942
Q18.12984
median11.2
Q313.4
95-th percentile16.74
Maximum50
Range45.10151
Interquartile range (IQR)5.27016

Descriptive statistics

Standard deviation6.5292073
Coefficient of variation (CV)0.56876291
Kurtosis25.598893
Mean11.479664
Median Absolute Deviation (MAD)2.8
Skewness4.3591976
Sum562.50355
Variance42.630548
MonotonicityNot monotonic
2023-12-10T23:42:50.505095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
14.0 4
 
8.2%
11.9419 4
 
8.2%
9.49524 2
 
4.1%
14.2 2
 
4.1%
14.8 1
 
2.0%
16.4 1
 
2.0%
13.1343 1
 
2.0%
11.7538 1
 
2.0%
10.7387 1
 
2.0%
4.93118 1
 
2.0%
Other values (31) 31
63.3%
ValueCountFrequency (%)
4.89849 1
2.0%
4.93118 1
2.0%
5.24103 1
2.0%
5.42831 1
2.0%
5.48211 1
2.0%
5.98128 1
2.0%
6.34221 1
2.0%
6.63368 1
2.0%
7.1097 1
2.0%
7.3964 1
2.0%
ValueCountFrequency (%)
50.0 1
 
2.0%
17.2 1
 
2.0%
16.9 1
 
2.0%
16.5 1
 
2.0%
16.4 1
 
2.0%
14.8 1
 
2.0%
14.2 2
4.1%
14.0 4
8.2%
13.4 1
 
2.0%
13.1343 1
 
2.0%

SHIP_OWNER_NM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing49
Missing (%)100.0%
Memory size573.0 B

DRAFT
Real number (ℝ)

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.046391
Minimum7.60386
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:50.606488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.60386
5-th percentile7.809592
Q18.19673
median15.645
Q321.7464
95-th percentile30
Maximum30
Range22.39614
Interquartile range (IQR)13.54967

Descriptive statistics

Standard deviation7.7876875
Coefficient of variation (CV)0.48532329
Kurtosis-1.1968086
Mean16.046391
Median Absolute Deviation (MAD)7.3784
Skewness0.40248975
Sum786.27318
Variance60.648076
MonotonicityNot monotonic
2023-12-10T23:42:50.719443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
30.0 5
 
10.2%
7.91159 4
 
8.2%
21.65 2
 
4.1%
10.1472 1
 
2.0%
9.93566 1
 
2.0%
8.17089 1
 
2.0%
8.18894 1
 
2.0%
7.87069 1
 
2.0%
8.20306 1
 
2.0%
7.64993 1
 
2.0%
Other values (31) 31
63.3%
ValueCountFrequency (%)
7.60386 1
 
2.0%
7.64993 1
 
2.0%
7.76886 1
 
2.0%
7.87069 1
 
2.0%
7.91159 4
8.2%
7.93361 1
 
2.0%
8.04752 1
 
2.0%
8.17089 1
 
2.0%
8.18894 1
 
2.0%
8.19673 1
 
2.0%
ValueCountFrequency (%)
30.0 5
10.2%
28.0 1
 
2.0%
25.1347 1
 
2.0%
23.8797 1
 
2.0%
22.9621 1
 
2.0%
22.956 1
 
2.0%
22.2449 1
 
2.0%
21.9861 1
 
2.0%
21.7464 1
 
2.0%
21.6637 1
 
2.0%

SHPYRD_NM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing49
Missing (%)100.0%
Memory size573.0 B

BULD_YR
Real number (ℝ)

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.7755
Minimum1985
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:50.810209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1990
Q12006
median2011
Q32018
95-th percentile2022
Maximum2022
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.699037
Coefficient of variation (CV)0.0053234984
Kurtosis-0.29387456
Mean2009.7755
Median Absolute Deviation (MAD)7
Skewness-0.77667268
Sum98479
Variance114.46939
MonotonicityNot monotonic
2023-12-10T23:42:50.907018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2022 10
20.4%
2010 7
14.3%
2018 3
 
6.1%
2014 3
 
6.1%
1994 2
 
4.1%
1985 2
 
4.1%
2015 2
 
4.1%
2017 2
 
4.1%
2008 2
 
4.1%
1995 2
 
4.1%
Other values (13) 14
28.6%
ValueCountFrequency (%)
1985 2
4.1%
1988 1
2.0%
1993 1
2.0%
1994 2
4.1%
1995 2
4.1%
1996 1
2.0%
1997 1
2.0%
1999 1
2.0%
2003 1
2.0%
2006 1
2.0%
ValueCountFrequency (%)
2022 10
20.4%
2020 1
 
2.0%
2018 3
 
6.1%
2017 2
 
4.1%
2015 2
 
4.1%
2014 3
 
6.1%
2013 1
 
2.0%
2012 1
 
2.0%
2011 2
 
4.1%
2010 7
14.3%

DDWGHT
Real number (ℝ)

Distinct45
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15346.755
Minimum732
Maximum53100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:51.013500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum732
5-th percentile1345
Q15477
median11546
Q322733
95-th percentile47212
Maximum53100
Range52368
Interquartile range (IQR)17256

Descriptive statistics

Standard deviation13245.383
Coefficient of variation (CV)0.86307382
Kurtosis1.4685843
Mean15346.755
Median Absolute Deviation (MAD)7146
Skewness1.3227762
Sum751991
Variance1.7544016 × 108
MonotonicityNot monotonic
2023-12-10T23:42:51.117720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
17430 4
 
8.2%
7482 2
 
4.1%
30910 1
 
2.0%
48220 1
 
2.0%
22388 1
 
2.0%
22733 1
 
2.0%
16648 1
 
2.0%
23003 1
 
2.0%
12427 1
 
2.0%
780 1
 
2.0%
Other values (35) 35
71.4%
ValueCountFrequency (%)
732 1
2.0%
780 1
2.0%
1235 1
2.0%
1510 1
2.0%
1589 1
2.0%
2322 1
2.0%
2852 1
2.0%
3280 1
2.0%
3979 1
2.0%
4400 1
2.0%
ValueCountFrequency (%)
53100 1
2.0%
50246 1
2.0%
48220 1
2.0%
45700 1
2.0%
32000 1
2.0%
30910 1
2.0%
24887 1
2.0%
24218 1
2.0%
23631 1
2.0%
23294 1
2.0%

DPTR_HMS
Date

Distinct18
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 12:00:00
Maximum2022-03-12 18:00:00
2023-12-10T23:42:51.210100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:42:51.300468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)

ARVL_HMS
Date

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-02-28 06:00:00
Maximum2022-07-17 18:00:00
2023-12-10T23:42:51.379792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:42:51.465301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.563788
Minimum-6.75431
Maximum63.054798
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)12.2%
Memory size573.0 B
2023-12-10T23:42:51.569128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.75431
5-th percentile-5.546724
Q125.0746
median34.3353
Q339.201302
95-th percentile45.841219
Maximum63.054798
Range69.809108
Interquartile range (IQR)14.126702

Descriptive statistics

Standard deviation16.12282
Coefficient of variation (CV)0.54535704
Kurtosis0.69289128
Mean29.563788
Median Absolute Deviation (MAD)5.882802
Skewness-1.0216566
Sum1448.6256
Variance259.94531
MonotonicityNot monotonic
2023-12-10T23:42:51.696483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
31.361601 1
 
2.0%
31.010401 1
 
2.0%
37.639 1
 
2.0%
31.6593 1
 
2.0%
24.067499 1
 
2.0%
41.421001 1
 
2.0%
38.227901 1
 
2.0%
39.313801 1
 
2.0%
63.054798 1
 
2.0%
35.764 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-6.75431 1
2.0%
-5.7234 1
2.0%
-5.5535 1
2.0%
-5.53656 1
2.0%
-2.77862 1
2.0%
-0.780818 1
2.0%
1.82342 1
2.0%
6.13687 1
2.0%
22.1591 1
2.0%
22.306999 1
2.0%
ValueCountFrequency (%)
63.054798 1
2.0%
54.536201 1
2.0%
47.115299 1
2.0%
43.930099 1
2.0%
41.786301 1
2.0%
41.777199 1
2.0%
41.7313 1
2.0%
41.421001 1
2.0%
40.8367 1
2.0%
40.368698 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.18042
Minimum7.4435
Maximum141.60001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:51.818129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.4435
5-th percentile21.3714
Q1105.357
median120.159
Q3132.498
95-th percentile140.692
Maximum141.60001
Range134.15651
Interquartile range (IQR)27.140999

Descriptive statistics

Standard deviation38.012213
Coefficient of variation (CV)0.35465633
Kurtosis1.4819948
Mean107.18042
Median Absolute Deviation (MAD)13.279998
Skewness-1.6501481
Sum5251.8404
Variance1444.9283
MonotonicityNot monotonic
2023-12-10T23:42:51.931768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
121.436996 1
 
2.0%
121.441002 1
 
2.0%
118.462997 1
 
2.0%
120.898003 1
 
2.0%
118.115997 1
 
2.0%
29.263 1
 
2.0%
118.258003 1
 
2.0%
25.6488 1
 
2.0%
7.4435 1
 
2.0%
134.406998 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
7.4435 1
2.0%
8.38902 1
2.0%
18.5198 1
2.0%
25.6488 1
2.0%
29.263 1
2.0%
29.2805 1
2.0%
39.316799 1
2.0%
56.270699 1
2.0%
93.522301 1
2.0%
102.561996 1
2.0%
ValueCountFrequency (%)
141.600006 1
2.0%
140.751007 1
2.0%
140.703995 1
2.0%
140.673996 1
2.0%
139.934998 1
2.0%
136.639999 1
2.0%
135.455994 1
2.0%
135.447998 1
2.0%
135.216995 1
2.0%
134.830002 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.523351
Minimum-5.98145
Maximum62.583401
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)10.2%
Memory size573.0 B
2023-12-10T23:42:52.039330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.98145
5-th percentile-3.137978
Q129.493
median34.2136
Q337.532398
95-th percentile43.07406
Maximum62.583401
Range68.564851
Interquartile range (IQR)8.039398

Descriptive statistics

Standard deviation15.331762
Coefficient of variation (CV)0.51930967
Kurtosis0.96727997
Mean29.523351
Median Absolute Deviation (MAD)4.425699
Skewness-1.1977668
Sum1446.6442
Variance235.06291
MonotonicityNot monotonic
2023-12-10T23:42:52.156667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
31.7516 1
 
2.0%
31.219999 1
 
2.0%
31.007401 1
 
2.0%
38.170898 1
 
2.0%
33.904099 1
 
2.0%
36.016399 1
 
2.0%
41.1819 1
 
2.0%
44.1045 1
 
2.0%
62.583401 1
 
2.0%
35.561501 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-5.98145 1
2.0%
-5.89361 1
2.0%
-3.23121 1
2.0%
-2.99813 1
2.0%
-0.142984 1
2.0%
0.466597 1
2.0%
0.471415 1
2.0%
1.05917 1
2.0%
24.135599 1
2.0%
25.5193 1
2.0%
ValueCountFrequency (%)
62.583401 1
2.0%
45.344101 1
2.0%
44.1045 1
2.0%
41.5284 1
2.0%
41.522499 1
2.0%
41.271 1
2.0%
41.1819 1
2.0%
40.7257 1
2.0%
40.310299 1
2.0%
39.940201 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.06195
Minimum-6.81702
Maximum140.701
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)2.0%
Memory size573.0 B
2023-12-10T23:42:52.262189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.81702
5-th percentile7.477386
Q1117.938
median121.66
Q3132.26401
95-th percentile140.185
Maximum140.701
Range147.51802
Interquartile range (IQR)14.326004

Descriptive statistics

Standard deviation40.192975
Coefficient of variation (CV)0.36853342
Kurtosis2.4993768
Mean109.06195
Median Absolute Deviation (MAD)10.604004
Skewness-1.952163
Sum5344.0357
Variance1615.4753
MonotonicityNot monotonic
2023-12-10T23:42:52.365068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
121.660004 1
 
2.0%
118.116997 1
 
2.0%
122.421997 1
 
2.0%
120.308998 1
 
2.0%
122.207001 1
 
2.0%
-6.81702 1
 
2.0%
132.264008 1
 
2.0%
28.6651 1
 
2.0%
6.91805 1
 
2.0%
135.313004 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-6.81702 1
2.0%
4.64083 1
2.0%
6.91805 1
2.0%
8.31639 1
2.0%
28.6651 1
2.0%
29.480101 1
2.0%
36.6213 1
2.0%
103.801003 1
2.0%
103.910004 1
2.0%
105.112999 1
2.0%
ValueCountFrequency (%)
140.701004 1
2.0%
140.498993 1
2.0%
140.477005 1
2.0%
139.746994 1
2.0%
139.567001 1
2.0%
136.690994 1
2.0%
135.455994 1
2.0%
135.313004 1
2.0%
134.830994 1
2.0%
134.677994 1
2.0%

PRFMC
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3121461 × 108
Minimum1.27135 × 108
Maximum1.36488 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:52.475378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.27135 × 108
5-th percentile1.27841 × 108
Q11.28965 × 108
median1.30992 × 108
Q31.32768 × 108
95-th percentile1.35437 × 108
Maximum1.36488 × 108
Range9353000
Interquartile range (IQR)3803000

Descriptive statistics

Standard deviation2509541.9
Coefficient of variation (CV)0.019125476
Kurtosis-0.81647066
Mean1.3121461 × 108
Median Absolute Deviation (MAD)2027000
Skewness0.25538069
Sum6.429516 × 109
Variance6.2978004 × 1012
MonotonicityStrictly increasing
2023-12-10T23:42:52.594601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
127135000 1
 
2.0%
133152000 1
 
2.0%
131286000 1
 
2.0%
131515000 1
 
2.0%
131566000 1
 
2.0%
132035000 1
 
2.0%
132036000 1
 
2.0%
132531000 1
 
2.0%
132729000 1
 
2.0%
132733000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
127135000 1
2.0%
127177000 1
2.0%
127773000 1
2.0%
127943000 1
2.0%
127969000 1
2.0%
128063000 1
2.0%
128075000 1
2.0%
128192000 1
2.0%
128364000 1
2.0%
128681000 1
2.0%
ValueCountFrequency (%)
136488000 1
2.0%
136179000 1
2.0%
135453000 1
2.0%
135413000 1
2.0%
134886000 1
2.0%
134271000 1
2.0%
134098000 1
2.0%
133985000 1
2.0%
133879000 1
2.0%
133835000 1
2.0%

FUEL_CNSMP_QTY
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7050537 × 109
Minimum1.63085 × 108
Maximum3.10871 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:52.981722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.63085 × 108
5-th percentile3.275218 × 108
Q11.41442 × 109
median1.71556 × 109
Q32.27824 × 109
95-th percentile3.022272 × 109
Maximum3.10871 × 109
Range2.945625 × 109
Interquartile range (IQR)8.6382 × 108

Descriptive statistics

Standard deviation7.6668304 × 108
Coefficient of variation (CV)0.44965332
Kurtosis-0.42678369
Mean1.7050537 × 109
Median Absolute Deviation (MAD)5.4025 × 108
Skewness-0.21325365
Sum8.354763 × 1010
Variance5.8780289 × 1017
MonotonicityNot monotonic
2023-12-10T23:42:53.098450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2098980000 1
 
2.0%
1302980000 1
 
2.0%
1523390000 1
 
2.0%
1552760000 1
 
2.0%
1427070000 1
 
2.0%
2328550000 1
 
2.0%
1414420000 1
 
2.0%
2278240000 1
 
2.0%
1813360000 1
 
2.0%
2409150000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
163085000 1
2.0%
238202000 1
2.0%
240853000 1
2.0%
457525000 1
2.0%
476783000 1
2.0%
575192000 1
2.0%
658292000 1
2.0%
821977000 1
2.0%
829869000 1
2.0%
945852000 1
2.0%
ValueCountFrequency (%)
3108710000 1
2.0%
3107250000 1
2.0%
3029620000 1
2.0%
3011250000 1
2.0%
2546270000 1
2.0%
2495820000 1
2.0%
2487100000 1
2.0%
2475660000 1
2.0%
2413050000 1
2.0%
2409150000 1
2.0%

NVGTN_DIST
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.000193
Minimum1.25229
Maximum24.1469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:42:53.218511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.25229
5-th percentile2.499564
Q110.7124
median13.1158
Q317.1903
95-th percentile22.61938
Maximum24.1469
Range22.89461
Interquartile range (IQR)6.4779

Descriptive statistics

Standard deviation5.8559338
Coefficient of variation (CV)0.45044974
Kurtosis-0.45620199
Mean13.000193
Median Absolute Deviation (MAD)3.8301
Skewness-0.20955994
Sum637.00948
Variance34.291961
MonotonicityNot monotonic
2023-12-10T23:42:53.334137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
16.5099 1
 
2.0%
9.78568 1
 
2.0%
11.6036 1
 
2.0%
11.8067 1
 
2.0%
10.8468 1
 
2.0%
17.6358 1
 
2.0%
10.7124 1
 
2.0%
17.1903 1
 
2.0%
13.6621 1
 
2.0%
18.1503 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1.25229 1
2.0%
1.77404 1
2.0%
1.83532 1
2.0%
3.49593 1
2.0%
3.66836 1
2.0%
4.33265 1
2.0%
5.14518 1
2.0%
6.27502 1
2.0%
6.49489 1
2.0%
7.25185 1
2.0%
ValueCountFrequency (%)
24.1469 1
2.0%
23.655 1
2.0%
22.9573 1
2.0%
22.1125 1
2.0%
19.5033 1
2.0%
19.4663 1
2.0%
19.4297 1
2.0%
19.2851 1
2.0%
18.472 1
2.0%
18.1503 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
2023-12-10T23:42:53.445474image/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
2023-12-10T23:42:53.585357image/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_LOPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
04133287909614804JIANG QUAN 6BULK CARRIER26.0171.614.8<NA>30.0<NA>20133091001-Jan-2022 12:00:0017-Jul-2022 18:00:0031.361601121.43699631.7516121.660004127135000209898000016.50992
14220403009465849WARTABULK CARRIER32.26182.017.2<NA>30.0<NA>20095310008-Jan-2022 06:00:0015-Jul-2022 12:00:0026.96750156.27069933.23120.863998127177000247566000019.46633
26130037148726155NEFTERUDOVOZ 50MOre or Oil Carrier13.46119.046.63368<NA>16.3974<NA>1985328001-Jan-2022 12:00:0005-Jun-2022 12:00:0047.11529939.31679945.34410136.62131277730008298690006.494894
33110001428315554TERTNESBULK CARRIER20.5121.811.6<NA>7.60386<NA>19851154601-Jan-2022 12:00:0005-Jun-2022 18:00:0054.53620118.519837.2697984.640831279430006582920005.145185
44144030709843455HENG HUI 58Chemical/Oil Product16.099.937.76755<NA>18.8269<NA>2018494501-Jan-2022 12:00:0017-Jul-2022 18:00:0022.1591114.15499936.066502122.780998127969000249582000019.50336
54134383909584580JIN FU XING 66BULK CARRIER24.2148.814.2<NA>8.2359<NA>20102363107-Jan-2022 12:00:0017-Jul-2022 12:00:0031.457701121.39600435.0648119.945128063000171809000013.4167
64310068169739147SYOUZAN MARUCement Carrier18.0119.799.36517<NA>21.4618<NA>2015729101-Jan-2022 18:00:0017-Jul-2022 00:00:0040.218102139.93499833.7495131.729996128075000302962000023.6558
74310106949839234KOSHO MARUChemical/Oil Product12.061.05.24103<NA>11.8232<NA>2017123502-Jan-2022 12:00:0017-Jul-2022 18:00:0034.0009131.74699435.2971139.746994128192000217515000016.96799
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