Overview

Dataset statistics

Number of variables21
Number of observations49
Missing cells91
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory185.7 B

Variable types

Numeric13
Text3
Categorical3
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000440

Alerts

SHIP_OWNER_NM has constant value ""Constant
CRG_TYP has constant value ""Constant
SHIP_HGHT is highly imbalanced (64.2%)Imbalance
SHIP_NM has 1 (2.0%) missing valuesMissing
SHIP_OWNER_NM has 48 (98.0%) missing valuesMissing
SHPYRD_NM has 42 (85.7%) missing valuesMissing
MMSI has unique valuesUnique
RN has unique valuesUnique
IMO_IDNTF_NO has 42 (85.7%) zerosZeros
SHIP_WDTH has 1 (2.0%) zerosZeros
SHIP_LNTH has 1 (2.0%) zerosZeros
DRAFT has 44 (89.8%) zerosZeros
BULD_YR has 35 (71.4%) zerosZeros
DDWGHT has 38 (77.6%) zerosZeros
DPTRP_LA has 4 (8.2%) zerosZeros
DPTRP_LO has 4 (8.2%) zerosZeros
DTNT_LA has 11 (22.4%) zerosZeros
DTNT_LO has 11 (22.4%) zerosZeros
FRGHT_CNVNC_QTY has 3 (6.1%) zerosZeros

Reproduction

Analysis started2023-12-10 14:39:02.306430
Analysis finished2023-12-10 14:39:02.548697
Duration0.24 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%
Mean2.0554002 × 108
Minimum2.0552179 × 108
Maximum2.05609 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:39:02.628652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0552179 × 108
5-th percentile2.0552353 × 108
Q12.0552889 × 108
median2.0553759 × 108
Q32.0554959 × 108
95-th percentile2.0555585 × 108
Maximum2.05609 × 108
Range87210
Interquartile range (IQR)20700

Descriptive statistics

Standard deviation14977.422
Coefficient of variation (CV)7.2868645 × 10-5
Kurtosis8.0061669
Mean2.0554002 × 108
Median Absolute Deviation (MAD)10700
Skewness2.0253841
Sum1.0071461 × 1010
Variance2.2432318 × 108
MonotonicityStrictly increasing
2023-12-10T23:39:02.746964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
205521790 1
 
2.0%
205551390 1
 
2.0%
205541790 1
 
2.0%
205542290 1
 
2.0%
205542590 1
 
2.0%
205543000 1
 
2.0%
205543790 1
 
2.0%
205544190 1
 
2.0%
205546690 1
 
2.0%
205547690 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
205521790 1
2.0%
205522990 1
2.0%
205523290 1
2.0%
205523890 1
2.0%
205524290 1
2.0%
205524390 1
2.0%
205525390 1
2.0%
205526000 1
2.0%
205526290 1
2.0%
205526390 1
2.0%
ValueCountFrequency (%)
205609000 1
2.0%
205559000 1
2.0%
205555890 1
2.0%
205555790 1
2.0%
205554990 1
2.0%
205554090 1
2.0%
205553090 1
2.0%
205553000 1
2.0%
205552090 1
2.0%
205552000 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1335720.6
Minimum0
Maximum9444649
Zeros42
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:39:02.847076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9380953
Maximum9444649
Range9444649
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3305892.3
Coefficient of variation (CV)2.474988
Kurtosis2.5409951
Mean1335720.6
Median Absolute Deviation (MAD)0
Skewness2.1066814
Sum65450307
Variance1.0928924 × 1013
MonotonicityNot monotonic
2023-12-10T23:39:02.935914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 42
85.7%
9361445 1
 
2.0%
9367918 1
 
2.0%
9177806 1
 
2.0%
9389643 1
 
2.0%
9444649 1
 
2.0%
9416733 1
 
2.0%
9292113 1
 
2.0%
ValueCountFrequency (%)
0 42
85.7%
9177806 1
 
2.0%
9292113 1
 
2.0%
9361445 1
 
2.0%
9367918 1
 
2.0%
9389643 1
 
2.0%
9416733 1
 
2.0%
9444649 1
 
2.0%
ValueCountFrequency (%)
9444649 1
 
2.0%
9416733 1
 
2.0%
9389643 1
 
2.0%
9367918 1
 
2.0%
9361445 1
 
2.0%
9292113 1
 
2.0%
9177806 1
 
2.0%
0 42
85.7%

SHIP_NM
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Memory size524.0 B
2023-12-10T23:39:03.118455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length7.3541667
Min length4

Characters and Unicode

Total characters353
Distinct characters29
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

Unique48 ?
Unique (%)100.0%

Sample

1st rowANTIBES
2nd rowTHALASSA
3rd rowHYDRUS
4th rowTRAFUCO 7
5th rowCAYMAN
ValueCountFrequency (%)
trafuco 2
 
3.6%
somtrans 2
 
3.6%
2 2
 
3.6%
antibes 1
 
1.8%
purity 1
 
1.8%
tripolis 1
 
1.8%
targa 1
 
1.8%
malia 1
 
1.8%
eupen 1
 
1.8%
marbella 1
 
1.8%
Other values (43) 43
76.8%
2023-12-10T23:39:03.445274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 53
15.0%
E 36
 
10.2%
R 30
 
8.5%
S 27
 
7.6%
T 23
 
6.5%
N 23
 
6.5%
I 21
 
5.9%
M 14
 
4.0%
L 14
 
4.0%
P 13
 
3.7%
Other values (19) 99
28.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 341
96.6%
Space Separator 8
 
2.3%
Decimal Number 4
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 53
15.5%
E 36
10.6%
R 30
 
8.8%
S 27
 
7.9%
T 23
 
6.7%
N 23
 
6.7%
I 21
 
6.2%
M 14
 
4.1%
L 14
 
4.1%
P 13
 
3.8%
Other values (15) 87
25.5%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
7 1
25.0%
9 1
25.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 341
96.6%
Common 12
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 53
15.5%
E 36
10.6%
R 30
 
8.8%
S 27
 
7.9%
T 23
 
6.7%
N 23
 
6.7%
I 21
 
6.2%
M 14
 
4.1%
L 14
 
4.1%
P 13
 
3.8%
Other values (15) 87
25.5%
Common
ValueCountFrequency (%)
8
66.7%
2 2
 
16.7%
7 1
 
8.3%
9 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 53
15.0%
E 36
 
10.2%
R 30
 
8.5%
S 27
 
7.6%
T 23
 
6.5%
N 23
 
6.5%
I 21
 
5.9%
M 14
 
4.0%
L 14
 
4.0%
P 13
 
3.7%
Other values (19) 99
28.0%

SHIP_KIND
Categorical

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
Inland Motor Tanker
19 
Inland Motor Tanker liquid cargo type C
15 
Inland Motor Tanker dry cargo as if liquid
3 
Inland Tanker
2 
LPG Tanker
2 
Other values (7)
8 

Length

Max length46
Median length42
Mean length26.510204
Min length4

Unique

Unique6 ?
Unique (%)12.2%

Sample

1st rowInland Motor Tanker
2nd rowInland Motor Tanker liquid cargo type C
3rd rowInland Motor Tanker
4th rowInland Motor Tanker
5th rowInland Motor Tanker liquid cargo type C

Common Values

ValueCountFrequency (%)
Inland Motor Tanker 19
38.8%
Inland Motor Tanker liquid cargo type C 15
30.6%
Inland Motor Tanker dry cargo as if liquid 3
 
6.1%
Inland Tanker 2
 
4.1%
LPG Tanker 2
 
4.1%
Floating Storage or Production 2
 
4.1%
LNG Tanker 1
 
2.0%
<NA> 1
 
2.0%
Inland Pushtow four barges at least one tanker 1
 
2.0%
Tanker 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2023-12-10T23:39:03.567312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tanker 46
20.5%
inland 41
18.3%
motor 38
17.0%
liquid 19
8.5%
cargo 19
8.5%
type 16
 
7.1%
c 15
 
6.7%
dry 3
 
1.3%
as 3
 
1.3%
if 3
 
1.3%
Other values (16) 21
9.4%

SHIP_WDTH
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.802041
Minimum0
Maximum48
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:39:03.664992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.82
Q111.4
median11.5
Q317.5
95-th percentile43.4
Maximum48
Range48
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation10.036132
Coefficient of variation (CV)0.63511622
Kurtosis3.7346008
Mean15.802041
Median Absolute Deviation (MAD)3.3
Skewness1.954336
Sum774.3
Variance100.72395
MonotonicityNot monotonic
2023-12-10T23:39:03.770656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
11.4 10
20.4%
15.0 5
 
10.2%
17.5 4
 
8.2%
43.4 3
 
6.1%
11.5 3
 
6.1%
17.0 2
 
4.1%
10.0 2
 
4.1%
17.6 1
 
2.0%
27.4 1
 
2.0%
6.3 1
 
2.0%
Other values (17) 17
34.7%
ValueCountFrequency (%)
0.0 1
2.0%
5.1 1
2.0%
5.5 1
2.0%
6.3 1
2.0%
8.2 1
2.0%
9.5 1
2.0%
9.6 1
2.0%
10.0 2
4.1%
10.5 1
2.0%
11.0 1
2.0%
ValueCountFrequency (%)
48.0 1
 
2.0%
43.4 3
6.1%
29.2 1
 
2.0%
27.4 1
 
2.0%
23.0 1
 
2.0%
20.3 1
 
2.0%
17.6 1
 
2.0%
17.5 4
8.2%
17.0 2
 
4.1%
15.0 5
10.2%

SHIP_LNTH
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.99224
Minimum0
Maximum280
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:39:03.860928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.56
Q186
median110
Q3135
95-th percentile273.6
Maximum280
Range280
Interquartile range (IQR)49

Descriptive statistics

Standard deviation60.496955
Coefficient of variation (CV)0.50841091
Kurtosis2.2469727
Mean118.99224
Median Absolute Deviation (MAD)25
Skewness0.96501579
Sum5830.62
Variance3659.8816
MonotonicityNot monotonic
2023-12-10T23:39:03.962235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
135.0 15
30.6%
110.0 12
24.5%
280.0 3
 
6.1%
86.0 3
 
6.1%
109.9 2
 
4.1%
172.02 1
 
2.0%
172.0 1
 
2.0%
264.0 1
 
2.0%
85.0 1
 
2.0%
47.6 1
 
2.0%
Other values (9) 9
18.4%
ValueCountFrequency (%)
0.0 1
2.0%
0.2 1
2.0%
18.8 1
2.0%
40.7 1
2.0%
47.5 1
2.0%
47.6 1
2.0%
60.0 1
2.0%
73.0 1
2.0%
81.0 1
2.0%
85.0 1
2.0%
ValueCountFrequency (%)
280.0 3
 
6.1%
264.0 1
 
2.0%
172.02 1
 
2.0%
172.0 1
 
2.0%
135.0 15
30.6%
110.0 12
24.5%
109.9 2
 
4.1%
106.0 1
 
2.0%
86.0 3
 
6.1%
85.0 1
 
2.0%

SHIP_HGHT
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
43 
26.0
 
3
18.2
 
2
23.2
 
1

Length

Max length4
Median length3
Mean length3.122449
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 43
87.8%
26.0 3
 
6.1%
18.2 2
 
4.1%
23.2 1
 
2.0%

Length

2023-12-10T23:39:04.079644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:39:04.177483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 43
87.8%
26.0 3
 
6.1%
18.2 2
 
4.1%
23.2 1
 
2.0%

SHIP_OWNER_NM
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing48
Missing (%)98.0%
Memory size524.0 B
2023-12-10T23:39:04.260011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowEURONAV
ValueCountFrequency (%)
euronav 1
100.0%
2023-12-10T23:39:04.470585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1
14.3%
U 1
14.3%
R 1
14.3%
O 1
14.3%
N 1
14.3%
A 1
14.3%
V 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1
14.3%
U 1
14.3%
R 1
14.3%
O 1
14.3%
N 1
14.3%
A 1
14.3%
V 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 7
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1
14.3%
U 1
14.3%
R 1
14.3%
O 1
14.3%
N 1
14.3%
A 1
14.3%
V 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1
14.3%
U 1
14.3%
R 1
14.3%
O 1
14.3%
N 1
14.3%
A 1
14.3%
V 1
14.3%

DRAFT
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1795918
Minimum0
Maximum16.7
Zeros44
Zeros (%)89.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:39:04.584948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10.4
Maximum16.7
Range16.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6418848
Coefficient of variation (CV)3.087411
Kurtosis8.5166449
Mean1.1795918
Median Absolute Deviation (MAD)0
Skewness3.0463568
Sum57.8
Variance13.263325
MonotonicityNot monotonic
2023-12-10T23:39:04.678576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 44
89.8%
10.8 1
 
2.0%
9.8 1
 
2.0%
9.0 1
 
2.0%
16.7 1
 
2.0%
11.5 1
 
2.0%
ValueCountFrequency (%)
0.0 44
89.8%
9.0 1
 
2.0%
9.8 1
 
2.0%
10.8 1
 
2.0%
11.5 1
 
2.0%
16.7 1
 
2.0%
ValueCountFrequency (%)
16.7 1
 
2.0%
11.5 1
 
2.0%
10.8 1
 
2.0%
9.8 1
 
2.0%
9.0 1
 
2.0%
0.0 44
89.8%

SHPYRD_NM
Text

MISSING 

Distinct4
Distinct (%)57.1%
Missing42
Missing (%)85.7%
Memory size524.0 B
2023-12-10T23:39:04.797688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length4
Mean length18.142857
Min length4

Characters and Unicode

Total characters127
Distinct characters25
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

Unique3 ?
Unique (%)42.9%

Sample

1st rowDSME
2nd rowHYUNDAI HEAVY INDUSTRIES
3rd rowMHI NAGASAKI SHIPYARD & ENGINE WORKS
4th rowDSME
5th rowDSME
ValueCountFrequency (%)
dsme 4
19.0%
3
14.3%
heavy 2
9.5%
industries 2
9.5%
hyundai 1
 
4.8%
mhi 1
 
4.8%
nagasaki 1
 
4.8%
shipyard 1
 
4.8%
engine 1
 
4.8%
works 1
 
4.8%
Other values (4) 4
19.0%
2023-12-10T23:39:05.026234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14
11.0%
S 14
11.0%
E 12
 
9.4%
I 12
 
9.4%
D 9
 
7.1%
N 8
 
6.3%
A 8
 
6.3%
M 6
 
4.7%
H 6
 
4.7%
U 5
 
3.9%
Other values (15) 33
26.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 109
85.8%
Space Separator 14
 
11.0%
Other Punctuation 3
 
2.4%
Dash Punctuation 1
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 14
12.8%
E 12
11.0%
I 12
11.0%
D 9
 
8.3%
N 8
 
7.3%
A 8
 
7.3%
M 6
 
5.5%
H 6
 
5.5%
U 5
 
4.6%
R 5
 
4.6%
Other values (11) 24
22.0%
Other Punctuation
ValueCountFrequency (%)
& 2
66.7%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 109
85.8%
Common 18
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 14
12.8%
E 12
11.0%
I 12
11.0%
D 9
 
8.3%
N 8
 
7.3%
A 8
 
7.3%
M 6
 
5.5%
H 6
 
5.5%
U 5
 
4.6%
R 5
 
4.6%
Other values (11) 24
22.0%
Common
ValueCountFrequency (%)
14
77.8%
& 2
 
11.1%
- 1
 
5.6%
, 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14
11.0%
S 14
11.0%
E 12
 
9.4%
I 12
 
9.4%
D 9
 
7.1%
N 8
 
6.3%
A 8
 
6.3%
M 6
 
4.7%
H 6
 
4.7%
U 5
 
3.9%
Other values (15) 33
26.0%

BULD_YR
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean571.34694
Minimum0
Maximum2015
Zeros35
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:39:05.125390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31999
95-th percentile2010.6
Maximum2015
Range2015
Interquartile range (IQR)1999

Descriptive statistics

Standard deviation912.86562
Coefficient of variation (CV)1.5977431
Kurtosis-1.0846621
Mean571.34694
Median Absolute Deviation (MAD)0
Skewness0.9798193
Sum27996
Variance833323.65
MonotonicityNot monotonic
2023-12-10T23:39:05.228089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 35
71.4%
2004 2
 
4.1%
2009 2
 
4.1%
2003 2
 
4.1%
2010 2
 
4.1%
2013 1
 
2.0%
1900 1
 
2.0%
2015 1
 
2.0%
1999 1
 
2.0%
2011 1
 
2.0%
ValueCountFrequency (%)
0 35
71.4%
1900 1
 
2.0%
1999 1
 
2.0%
2003 2
 
4.1%
2004 2
 
4.1%
2006 1
 
2.0%
2009 2
 
4.1%
2010 2
 
4.1%
2011 1
 
2.0%
2013 1
 
2.0%
ValueCountFrequency (%)
2015 1
2.0%
2013 1
2.0%
2011 1
2.0%
2010 2
4.1%
2009 2
4.1%
2006 1
2.0%
2004 2
4.1%
2003 2
4.1%
1999 1
2.0%
1900 1
2.0%

DDWGHT
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9959.551
Minimum0
Maximum157714
Zeros38
Zeros (%)77.6%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:39:05.318413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile83149.6
Maximum157714
Range157714
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29782.081
Coefficient of variation (CV)2.9903035
Kurtosis13.710326
Mean9959.551
Median Absolute Deviation (MAD)0
Skewness3.5998769
Sum488018
Variance8.8697232 × 108
MonotonicityNot monotonic
2023-12-10T23:39:05.403453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 38
77.6%
6350 1
 
2.0%
2999 1
 
2.0%
83200 1
 
2.0%
6000 1
 
2.0%
2995 1
 
2.0%
29121 1
 
2.0%
4135 1
 
2.0%
83166 1
 
2.0%
83125 1
 
2.0%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
0 38
77.6%
2995 1
 
2.0%
2999 1
 
2.0%
4135 1
 
2.0%
6000 1
 
2.0%
6350 1
 
2.0%
29121 1
 
2.0%
29213 1
 
2.0%
83125 1
 
2.0%
83166 1
 
2.0%
ValueCountFrequency (%)
157714 1
2.0%
83200 1
2.0%
83166 1
2.0%
83125 1
2.0%
29213 1
2.0%
29121 1
2.0%
6350 1
2.0%
6000 1
2.0%
4135 1
2.0%
2999 1
2.0%

DPTR_HMS
Date

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:00
Maximum2021-01-04 14:06:38
2023-12-10T23:39:05.499680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:39:05.603735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

ARVL_HMS
Date

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-19 08:50:25
Maximum2021-10-13 23:59:05
2023-12-10T23:39:05.701070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:39:05.801539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

DPTRP_LA
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.150196
Minimum-34.241699
Maximum53.428299
Zeros4
Zeros (%)8.2%
Negative2
Negative (%)4.1%
Memory size573.0 B
2023-12-10T23:39:05.898990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-34.241699
5-th percentile0
Q150.822899
median51.2715
Q351.889
95-th percentile52.42002
Maximum53.428299
Range87.669998
Interquartile range (IQR)1.066101

Descriptive statistics

Standard deviation22.431202
Coefficient of variation (CV)0.5451056
Kurtosis3.2846436
Mean41.150196
Median Absolute Deviation (MAD)0.607799
Skewness-2.0565857
Sum2016.3596
Variance503.15884
MonotonicityNot monotonic
2023-12-10T23:39:05.995169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0 4
 
8.2%
51.253101 2
 
4.1%
52.409901 1
 
2.0%
51.290501 1
 
2.0%
51.892399 1
 
2.0%
-28.153 1
 
2.0%
51.968201 1
 
2.0%
52.4072 1
 
2.0%
51.900902 1
 
2.0%
51.889 1
 
2.0%
Other values (35) 35
71.4%
ValueCountFrequency (%)
-34.241699 1
 
2.0%
-28.153 1
 
2.0%
0.0 4
8.2%
6.33221 1
 
2.0%
16.942301 1
 
2.0%
17.624701 1
 
2.0%
25.031099 1
 
2.0%
50.755001 1
 
2.0%
50.789001 1
 
2.0%
50.822899 1
 
2.0%
ValueCountFrequency (%)
53.428299 1
2.0%
52.427299 1
2.0%
52.4207 1
2.0%
52.418999 1
2.0%
52.409901 1
2.0%
52.4072 1
2.0%
51.968201 1
2.0%
51.9445 1
2.0%
51.932201 1
2.0%
51.930302 1
2.0%

DPTRP_LO
Real number (ℝ)

ZEROS 

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4137098
Minimum-74.851303
Maximum93.208702
Zeros4
Zeros (%)8.2%
Negative3
Negative (%)6.1%
Memory size573.0 B
2023-12-10T23:39:06.091276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-74.851303
5-th percentile-11.690281
Q14.015
median4.33814
Q34.80149
95-th percentile27.55088
Maximum93.208702
Range168.06
Interquartile range (IQR)0.78649

Descriptive statistics

Standard deviation21.653738
Coefficient of variation (CV)4.9060177
Kurtosis10.280809
Mean4.4137098
Median Absolute Deviation (MAD)0.46335
Skewness0.21204323
Sum216.27178
Variance468.88439
MonotonicityNot monotonic
2023-12-10T23:39:06.440950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 4
 
8.2%
4.84187 1
 
2.0%
4.33814 1
 
2.0%
4.30835 1
 
2.0%
4.36765 1
 
2.0%
36.1964 1
 
2.0%
4.03732 1
 
2.0%
4.8495 1
 
2.0%
4.35585 1
 
2.0%
4.31721 1
 
2.0%
Other values (36) 36
73.5%
ValueCountFrequency (%)
-74.851303 1
 
2.0%
-58.7603 1
 
2.0%
-19.483801 1
 
2.0%
0.0 4
8.2%
3.19274 1
 
2.0%
3.20276 1
 
2.0%
3.71435 1
 
2.0%
3.71448 1
 
2.0%
3.80088 1
 
2.0%
4.015 1
 
2.0%
ValueCountFrequency (%)
93.208702 1
2.0%
55.067402 1
2.0%
36.1964 1
2.0%
14.5826 1
2.0%
7.00216 1
2.0%
6.05548 1
2.0%
5.689 1
2.0%
5.67667 1
2.0%
5.48833 1
2.0%
4.8495 1
2.0%

DTNT_LA
Real number (ℝ)

ZEROS 

Distinct38
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.02877
Minimum-34.241699
Maximum52.574402
Zeros11
Zeros (%)22.4%
Negative2
Negative (%)4.1%
Memory size573.0 B
2023-12-10T23:39:06.545707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-34.241699
5-th percentile0
Q10
median51.185699
Q351.815601
95-th percentile52.227578
Maximum52.574402
Range86.816101
Interquartile range (IQR)51.815601

Descriptive statistics

Standard deviation24.883712
Coefficient of variation (CV)0.71037928
Kurtosis-0.28671555
Mean35.02877
Median Absolute Deviation (MAD)0.726902
Skewness-1.0945757
Sum1716.4097
Variance619.19913
MonotonicityNot monotonic
2023-12-10T23:39:06.647502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.0 11
 
22.4%
51.252998 2
 
4.1%
51.943401 1
 
2.0%
51.945999 1
 
2.0%
47.557899 1
 
2.0%
22.785801 1
 
2.0%
51.4464 1
 
2.0%
50.983898 1
 
2.0%
51.8993 1
 
2.0%
52.418701 1
 
2.0%
Other values (28) 28
57.1%
ValueCountFrequency (%)
-34.241699 1
 
2.0%
-12.9067 1
 
2.0%
0.0 11
22.4%
22.785801 1
 
2.0%
24.148701 1
 
2.0%
24.4193 1
 
2.0%
47.557899 1
 
2.0%
48.567501 1
 
2.0%
49.463402 1
 
2.0%
50.792999 1
 
2.0%
ValueCountFrequency (%)
52.574402 1
2.0%
52.418701 1
2.0%
52.415298 1
2.0%
51.945999 1
2.0%
51.943401 1
2.0%
51.912601 1
2.0%
51.907902 1
2.0%
51.8993 1
2.0%
51.888802 1
2.0%
51.880901 1
2.0%

DTNT_LO
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9608212
Minimum-88.173401
Maximum70.0448
Zeros11
Zeros (%)22.4%
Negative3
Negative (%)6.1%
Memory size573.0 B
2023-12-10T23:39:06.743365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-88.173401
5-th percentile-23.164021
Q10
median4.30976
Q34.66643
95-th percentile8.18626
Maximum70.0448
Range158.2182
Interquartile range (IQR)4.66643

Descriptive statistics

Standard deviation20.930106
Coefficient of variation (CV)10.674153
Kurtosis10.390307
Mean1.9608212
Median Absolute Deviation (MAD)0.51485
Skewness-1.3626147
Sum96.080239
Variance438.06936
MonotonicityNot monotonic
2023-12-10T23:39:06.851333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 11
 
22.4%
4.1757 1
 
2.0%
-58.760399 1
 
2.0%
7.63842 1
 
2.0%
70.0448 1
 
2.0%
3.71903 1
 
2.0%
4.38594 1
 
2.0%
4.22325 1
 
2.0%
4.77472 1
 
2.0%
4.09004 1
 
2.0%
Other values (29) 29
59.2%
ValueCountFrequency (%)
-88.173401 1
 
2.0%
-58.760399 1
 
2.0%
-38.606701 1
 
2.0%
0.0 11
22.4%
3.71307 1
 
2.0%
3.71903 1
 
2.0%
3.79491 1
 
2.0%
3.80365 1
 
2.0%
4.09004 1
 
2.0%
4.1757 1
 
2.0%
ValueCountFrequency (%)
70.0448 1
2.0%
52.7383 1
2.0%
8.44116 1
2.0%
7.80391 1
2.0%
7.63842 1
2.0%
6.98384 1
2.0%
5.86911 1
2.0%
5.68837 1
2.0%
4.78674 1
2.0%
4.77472 1
2.0%

CRG_TYP
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

2023-12-10T23:39:06.952072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:39:07.026305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

FRGHT_CNVNC_QTY
Real number (ℝ)

ZEROS 

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.494241 × 108
Minimum0
Maximum8.83875 × 109
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:39:07.118103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.34992 × 10-35
Q12405810
median15592000
Q324547700
95-th percentile3.083298 × 109
Maximum8.83875 × 109
Range8.83875 × 109
Interquartile range (IQR)22141890

Descriptive statistics

Standard deviation1.5307036 × 109
Coefficient of variation (CV)3.4059223
Kurtosis20.067714
Mean4.494241 × 108
Median Absolute Deviation (MAD)12489310
Skewness4.2775455
Sum2.2021781 × 1010
Variance2.3430534 × 1018
MonotonicityNot monotonic
2023-12-10T23:39:07.233429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.0 3
 
6.1%
1882800.0 1
 
2.0%
33328900.0 1
 
2.0%
24400400.0 1
 
2.0%
1689790000.0 1
 
2.0%
2.4530200000000003e-34 1
 
2.0%
3102690.0 1
 
2.0%
2.08748e-34 1
 
2.0%
15592000.0 1
 
2.0%
4313380.0 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
0.0 3
6.1%
2.08748e-34 1
 
2.0%
2.4530200000000003e-34 1
 
2.0%
2.9257e-34 1
 
2.0%
0.101905 1
 
2.0%
939877.0 1
 
2.0%
1086820.0 1
 
2.0%
1374550.0 1
 
2.0%
1530690.0 1
 
2.0%
1882800.0 1
 
2.0%
ValueCountFrequency (%)
8838750000.0 1
2.0%
4793000000.0 1
2.0%
3377230000.0 1
2.0%
2642400000.0 1
2.0%
1689790000.0 1
2.0%
97270400.0 1
2.0%
43757300.0 1
2.0%
40400200.0 1
2.0%
38263100.0 1
2.0%
33328900.0 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
2023-12-10T23:39:07.346498image/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
2023-12-10T23:39:07.488380image/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_LOCRG_TYPFRGHT_CNVNC_QTYRN
02055217900ANTIBESInland Motor Tanker15.0135.00.0<NA>0.0<NA>2013635001-Jan-2021 00:04:0013-Oct-2021 23:55:0152.4099014.8418751.9434014.175701882800.02
12055229900THALASSAInland Motor Tanker liquid cargo type C11.4110.00.0<NA>0.0<NA>0002-Jan-2021 06:19:1513-Oct-2021 17:25:0050.7890015.68950.7929995.6883703517580.03
22055232900HYDRUSInland Motor Tanker13.0110.00.0<NA>0.0<NA>0001-Jan-2021 00:02:1613-Oct-2021 23:58:040.00.00.00.001086820.04
32055238900TRAFUCO 7Inland Motor Tanker8.260.00.0<NA>0.0<NA>0001-Jan-2021 00:18:1513-Oct-2021 23:59:000.00.051.25564.377820939877.05
42055242900CAYMANInland Motor Tanker liquid cargo type C11.4109.90.0<NA>0.0<NA>2004299901-Jan-2021 00:00:0913-Oct-2021 23:55:0051.94454.1733552.5744025.86911024390500.06
52055243900SOMTRANS XXVIIIInland Tanker15.0135.00.0<NA>0.0<NA>0001-Jan-2021 00:08:0813-Oct-2021 23:59:0052.4272994.743630.00.002405810.07
62055253900ANTARESInland Motor Tanker liquid cargo type C15.0135.00.0<NA>0.0<NA>0001-Jan-2021 00:05:3813-Oct-2021 23:59:0551.44873.7144851.24864.3762505714190.08
72055260009361445EXPRESSLNG Tanker43.4280.026.0<NA>0.0DSME20098320001-Jan-2021 00:02:2113-Oct-2021 23:08:0325.03109955.06740224.14870152.738303377230000.09
82055262900SOMTRANS XXIXInland Motor Tanker liquid cargo type C15.0135.00.0<NA>0.0<NA>0001-Jan-2021 00:02:4313-Oct-2021 23:55:0351.9322014.1568951.8888024.37034020252100.010
92055263900STRAUSSInland Motor Tanker liquid cargo type C20.3109.90.0<NA>0.0<NA>0001-Jan-2021 00:05:0313-Oct-2021 23:58:0551.9303024.2008451.9079024.41636022758400.011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOCRG_TYPFRGHT_CNVNC_QTYRN
392055520009389643EXPEDIENTFloating Storage or Production43.4280.026.0<NA>9.8DSME20108316601-Jan-2021 01:04:4813-Oct-2021 23:47:01-34.241699-58.7603-34.241699-58.760399043757300.041
402055520900BITUMINA 2Inland Motor Tanker liquid cargo type C11.5110.00.0<NA>0.0<NA>0001-Jan-2021 00:02:4913-Oct-2021 23:55:0051.2985994.2958149.4634028.4411608696850.042
412055530009444649EXEMPLARFloating Storage or Production43.4280.026.0<NA>9.0DSME20108312501-Jan-2021 01:08:1913-Oct-2021 23:52:026.3322193.208702-12.9067-38.60670104793000000.043
422055530900TASMANZEEInland Motor Tanker liquid cargo type C17.0135.00.0<NA>0.0<NA>0001-Jan-2021 00:02:5413-Oct-2021 23:55:0351.4487993.7143551.4704023.71307021116300.044
432055540900BRABOInland Motor Tanker5.147.60.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 23:57:0551.8049014.6332851.2529984.3828101530690.045
442055549900LIBURNAInland Motor Tanker liquid cargo type C9.585.00.0<NA>0.0<NA>0001-Jan-2021 00:00:2313-Oct-2021 23:55:0051.2092023.8008851.8809014.2656701374550.046
452055557900EMMA 2Inland Motor Tanker11.4110.00.0<NA>0.0<NA>0001-Jan-2021 00:03:0313-Oct-2021 23:40:0450.8228997.0021651.8791014.30989038263100.047
462055558900BRILJANTInland Motor Tanker liquid cargo type C23.0135.00.0<NA>0.0<NA>0001-Jan-2021 00:01:4213-Oct-2021 23:58:0551.52154.0160651.2989014.32621024014500.048
472055590009416733FRATERNITYCrude Oil Tanker48.0264.023.2EURONAV16.7SAMSUNG SHIPBUILDING & HEAVY INDUSTRIES - GEOJE, KR200915771401-Jan-2021 00:00:0513-Oct-2021 23:35:0516.942301-19.48380124.4193-88.17340108838750000.049
482056090009292113SOMBEKELPG Tanker29.2172.018.2<NA>11.5DSME20062921301-Jan-2021 00:03:2013-Oct-2021 23:56:0217.624701-74.8513030.00.002642400000.050