Overview

Dataset statistics

Number of variables22
Number of observations49
Missing cells65
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory194.7 B

Variable types

Numeric14
Text2
Categorical4
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000347

Alerts

SHIP_HGHT is highly imbalanced (78.5%)Imbalance
DRAFT is highly imbalanced (78.5%)Imbalance
SHIP_NM has 19 (38.8%) missing valuesMissing
SHIP_OWNER_NM has 46 (93.9%) missing valuesMissing
MMSI has unique valuesUnique
AVE_VE has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique
IMO_IDNTF_NO has 8 (16.3%) zerosZeros
SHIP_WDTH has 19 (38.8%) zerosZeros
SHIP_LNTH has 19 (38.8%) zerosZeros
BULD_YR has 4 (8.2%) zerosZeros
DDWGHT has 22 (44.9%) zerosZeros
DPTRP_LA has 6 (12.2%) zerosZeros
DPTRP_LO has 6 (12.2%) zerosZeros
DTNT_LA has 17 (34.7%) zerosZeros
DTNT_LO has 17 (34.7%) zerosZeros
MAX_VE has 1 (2.0%) zerosZeros
AVE_VE has 1 (2.0%) zerosZeros
NVGTN_DIST has 1 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:47:07.783505
Analysis finished2023-12-10 14:47:08.080670
Duration0.3 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%
Mean4.3827475 × 108
Minimum4.310164 × 108
Maximum6.0793733 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:08.156370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.310164 × 108
5-th percentile4.3101674 × 108
Q14.310172 × 108
median4.31057 × 108
Q34.3110028 × 108
95-th percentile4.3110097 × 108
Maximum6.0793733 × 108
Range1.7692094 × 108
Interquartile range (IQR)83087

Descriptive statistics

Standard deviation35349240
Coefficient of variation (CV)0.080655432
Kurtosis21.827021
Mean4.3827475 × 108
Median Absolute Deviation (MAD)40281
Skewness4.7892619
Sum2.1475463 × 1010
Variance1.2495687 × 1015
MonotonicityStrictly increasing
2023-12-10T23:47:08.297610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
431016396 1
 
2.0%
431100372 1
 
2.0%
431100101 1
 
2.0%
431100109 1
 
2.0%
431100146 1
 
2.0%
431100181 1
 
2.0%
431100187 1
 
2.0%
431100205 1
 
2.0%
431100206 1
 
2.0%
431100271 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
431016396 1
2.0%
431016719 1
2.0%
431016729 1
2.0%
431016755 1
2.0%
431016759 1
2.0%
431016774 1
2.0%
431016819 1
2.0%
431016922 1
2.0%
431016957 1
2.0%
431016994 1
2.0%
ValueCountFrequency (%)
607937333 1
2.0%
607820333 1
2.0%
431100969 1
2.0%
431100967 1
2.0%
431100966 1
2.0%
431100938 1
2.0%
431100931 1
2.0%
431100923 1
2.0%
431100903 1
2.0%
431100878 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

ZEROS 

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7658644.1
Minimum0
Maximum9925411
Zeros8
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:08.426298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19086746
median9241499
Q39909390
95-th percentile9920225.4
Maximum9925411
Range9925411
Interquartile range (IQR)822644

Descriptive statistics

Standard deviation3724931.5
Coefficient of variation (CV)0.48636957
Kurtosis0.45760566
Mean7658644.1
Median Absolute Deviation (MAD)666427
Skewness-1.5280818
Sum3.7527356 × 108
Variance1.3875114 × 1013
MonotonicityNot monotonic
2023-12-10T23:47:08.548197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 8
 
16.3%
9920289 1
 
2.0%
9195262 1
 
2.0%
9124067 1
 
2.0%
9119127 1
 
2.0%
9136761 1
 
2.0%
9122253 1
 
2.0%
9152337 1
 
2.0%
9146857 1
 
2.0%
9033749 1
 
2.0%
Other values (32) 32
65.3%
ValueCountFrequency (%)
0 8
16.3%
2081361 1
 
2.0%
2424563 1
 
2.0%
9033749 1
 
2.0%
9067180 1
 
2.0%
9086746 1
 
2.0%
9110078 1
 
2.0%
9119127 1
 
2.0%
9122253 1
 
2.0%
9124067 1
 
2.0%
ValueCountFrequency (%)
9925411 1
2.0%
9920289 1
2.0%
9920277 1
2.0%
9920148 1
2.0%
9918353 1
2.0%
9914228 1
2.0%
9914046 1
2.0%
9914034 1
2.0%
9912919 1
2.0%
9912579 1
2.0%

SHIP_NM
Text

MISSING 

Distinct30
Distinct (%)100.0%
Missing19
Missing (%)38.8%
Memory size524.0 B
2023-12-10T23:47:08.709039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length11.933333
Min length5

Characters and Unicode

Total characters358
Distinct characters34
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

Unique30 ?
Unique (%)100.0%

Sample

1st rowK A M U I M A R U
2nd rowASUZAN MARU
3rd rowHOUREI MARU
4th rowTENRYU MARU
5th rowENERGY INNOVATOR
ValueCountFrequency (%)
maru 21
30.9%
no.8 2
 
2.9%
m 2
 
2.9%
u 2
 
2.9%
a 2
 
2.9%
nittan 2
 
2.9%
shinsui 1
 
1.5%
star 1
 
1.5%
phenix 1
 
1.5%
tokuyo 1
 
1.5%
Other values (33) 33
48.5%
2023-12-10T23:47:09.014261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 44
12.3%
U 41
11.5%
38
10.6%
R 32
 
8.9%
O 27
 
7.5%
M 26
 
7.3%
N 22
 
6.1%
K 16
 
4.5%
I 16
 
4.5%
S 14
 
3.9%
Other values (24) 82
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 307
85.8%
Space Separator 38
 
10.6%
Decimal Number 8
 
2.2%
Other Punctuation 5
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 44
14.3%
U 41
13.4%
R 32
10.4%
O 27
8.8%
M 26
8.5%
N 22
 
7.2%
K 16
 
5.2%
I 16
 
5.2%
S 14
 
4.6%
T 14
 
4.6%
Other values (16) 55
17.9%
Decimal Number
ValueCountFrequency (%)
8 2
25.0%
5 2
25.0%
7 1
12.5%
6 1
12.5%
2 1
12.5%
1 1
12.5%
Space Separator
ValueCountFrequency (%)
38
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 307
85.8%
Common 51
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 44
14.3%
U 41
13.4%
R 32
10.4%
O 27
8.8%
M 26
8.5%
N 22
 
7.2%
K 16
 
5.2%
I 16
 
5.2%
S 14
 
4.6%
T 14
 
4.6%
Other values (16) 55
17.9%
Common
ValueCountFrequency (%)
38
74.5%
. 5
 
9.8%
8 2
 
3.9%
5 2
 
3.9%
7 1
 
2.0%
6 1
 
2.0%
2 1
 
2.0%
1 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 44
12.3%
U 41
11.5%
38
10.6%
R 32
 
8.9%
O 27
 
7.5%
M 26
 
7.3%
N 22
 
6.1%
K 16
 
4.5%
I 16
 
4.5%
S 14
 
3.9%
Other values (24) 82
22.9%

SHIP_KIND
Categorical

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
<NA>
19 
Oil Products Tanker
11 
Tanker
3 
Chemical Tanker
3 
LPG Tanker
3 
Other values (7)
10 

Length

Max length28
Median length25
Mean length11.489796
Min length4

Unique

Unique4 ?
Unique (%)8.2%

Sample

1st row<NA>
2nd row<NA>
3rd rowTanker
4th rowAsphalt or Bitumen Tanker
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 19
38.8%
Oil Products Tanker 11
22.4%
Tanker 3
 
6.1%
Chemical Tanker 3
 
6.1%
LPG Tanker 3
 
6.1%
LNG Tanker 2
 
4.1%
Oil or Chemical Tanker 2
 
4.1%
Crude Oil Tanker 2
 
4.1%
Asphalt or Bitumen Tanker 1
 
2.0%
Bunkering Tanker 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2023-12-10T23:47:09.127569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tanker 28
29.8%
na 19
20.2%
oil 15
16.0%
products 11
 
11.7%
chemical 5
 
5.3%
lpg 3
 
3.2%
or 3
 
3.2%
lng 2
 
2.1%
crude 2
 
2.1%
asphalt 1
 
1.1%
Other values (5) 5
 
5.3%

SHIP_WDTH
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.709592
Minimum0
Maximum60
Zeros19
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:09.217821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q315.6
95-th percentile47.7
Maximum60
Range60
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation14.608056
Coefficient of variation (CV)1.2475291
Kurtosis4.3430225
Mean11.709592
Median Absolute Deviation (MAD)7.8
Skewness2.0156641
Sum573.77
Variance213.3953
MonotonicityNot monotonic
2023-12-10T23:47:09.316304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 19
38.8%
12.0 4
 
8.2%
10.0 3
 
6.1%
16.0 3
 
6.1%
17.2 2
 
4.1%
60.0 2
 
4.1%
13.0 2
 
4.1%
15.6 2
 
4.1%
9.62 1
 
2.0%
18.8 1
 
2.0%
Other values (10) 10
20.4%
ValueCountFrequency (%)
0.0 19
38.8%
9.0 1
 
2.0%
9.62 1
 
2.0%
10.0 3
 
6.1%
11.0 1
 
2.0%
11.2 1
 
2.0%
11.5 1
 
2.0%
12.0 4
 
8.2%
13.0 2
 
4.1%
15.0 1
 
2.0%
ValueCountFrequency (%)
60.0 2
4.1%
49.0 1
 
2.0%
45.75 1
 
2.0%
24.6 1
 
2.0%
18.8 1
 
2.0%
17.2 2
4.1%
16.0 3
6.1%
15.6 2
4.1%
15.5 1
 
2.0%
15.2 1
 
2.0%

SHIP_LNTH
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.904694
Minimum0
Maximum333
Zeros19
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:09.419040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median64.9
Q3104.81
95-th percentile293.14
Maximum333
Range333
Interquartile range (IQR)104.81

Descriptive statistics

Standard deviation85.573353
Coefficient of variation (CV)1.1900941
Kurtosis3.2054756
Mean71.904694
Median Absolute Deviation (MAD)47.6
Skewness1.7562304
Sum3523.33
Variance7322.7987
MonotonicityNot monotonic
2023-12-10T23:47:09.528700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 19
38.8%
105.0 3
 
6.1%
333.0 2
 
4.1%
69.0 1
 
2.0%
64.9 1
 
2.0%
112.5 1
 
2.0%
149.9 1
 
2.0%
104.94 1
 
2.0%
104.47 1
 
2.0%
64.52 1
 
2.0%
Other values (18) 18
36.7%
ValueCountFrequency (%)
0.0 19
38.8%
46.0 1
 
2.0%
55.0 1
 
2.0%
59.22 1
 
2.0%
60.0 1
 
2.0%
64.52 1
 
2.0%
64.9 1
 
2.0%
69.0 1
 
2.0%
70.76 1
 
2.0%
72.0 1
 
2.0%
ValueCountFrequency (%)
333.0 2
4.1%
299.9 1
 
2.0%
283.0 1
 
2.0%
149.9 1
 
2.0%
112.5 1
 
2.0%
109.61 1
 
2.0%
105.0 3
6.1%
104.96 1
 
2.0%
104.94 1
 
2.0%
104.81 1
 
2.0%

SHIP_HGHT
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
46 
25.5
 
1
13.1
 
1
10.4
 
1

Length

Max length4
Median length3
Mean length3.0612245
Min length3

Unique

Unique3 ?
Unique (%)6.1%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 46
93.9%
25.5 1
 
2.0%
13.1 1
 
2.0%
10.4 1
 
2.0%

Length

2023-12-10T23:47:09.644524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:47:09.752110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46
93.9%
25.5 1
 
2.0%
13.1 1
 
2.0%
10.4 1
 
2.0%

SHIP_OWNER_NM
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing46
Missing (%)93.9%
Memory size524.0 B
2023-12-10T23:47:09.856756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length2
Mean length8.6666667
Min length2

Characters and Unicode

Total characters26
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowNYK LNG SHIPMANAGEMENT
2nd rowQD
3rd rowQD
ValueCountFrequency (%)
qd 2
40.0%
nyk 1
20.0%
lng 1
20.0%
shipmanagement 1
20.0%
2023-12-10T23:47:10.097997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 4
15.4%
Q 2
 
7.7%
D 2
 
7.7%
2
 
7.7%
G 2
 
7.7%
M 2
 
7.7%
A 2
 
7.7%
E 2
 
7.7%
Y 1
 
3.8%
K 1
 
3.8%
Other values (6) 6
23.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24
92.3%
Space Separator 2
 
7.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 4
16.7%
Q 2
 
8.3%
D 2
 
8.3%
G 2
 
8.3%
M 2
 
8.3%
A 2
 
8.3%
E 2
 
8.3%
Y 1
 
4.2%
K 1
 
4.2%
L 1
 
4.2%
Other values (5) 5
20.8%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24
92.3%
Common 2
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4
16.7%
Q 2
 
8.3%
D 2
 
8.3%
G 2
 
8.3%
M 2
 
8.3%
A 2
 
8.3%
E 2
 
8.3%
Y 1
 
4.2%
K 1
 
4.2%
L 1
 
4.2%
Other values (5) 5
20.8%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4
15.4%
Q 2
 
7.7%
D 2
 
7.7%
2
 
7.7%
G 2
 
7.7%
M 2
 
7.7%
A 2
 
7.7%
E 2
 
7.7%
Y 1
 
3.8%
K 1
 
3.8%
Other values (6) 6
23.1%

DRAFT
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
46 
11.2
 
1
9.3
 
1
7.5
 
1

Length

Max length4
Median length3
Mean length3.0204082
Min length3

Unique

Unique3 ?
Unique (%)6.1%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 46
93.9%
11.2 1
 
2.0%
9.3 1
 
2.0%
7.5 1
 
2.0%

Length

2023-12-10T23:47:10.213799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:47:10.299318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46
93.9%
11.2 1
 
2.0%
9.3 1
 
2.0%
7.5 1
 
2.0%

SHPYRD_NM
Categorical

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
<NA>
27 
HYUNDAI HEAVY INDUSTRIES
19 
QD
 
2
KAWASAKI SAKAIDE WORKS
 
1

Length

Max length24
Median length4
Mean length12.040816
Min length2

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowHYUNDAI HEAVY INDUSTRIES
2nd rowHYUNDAI HEAVY INDUSTRIES
3rd row<NA>
4th row<NA>
5th rowHYUNDAI HEAVY INDUSTRIES

Common Values

ValueCountFrequency (%)
<NA> 27
55.1%
HYUNDAI HEAVY INDUSTRIES 19
38.8%
QD 2
 
4.1%
KAWASAKI SAKAIDE WORKS 1
 
2.0%

Length

2023-12-10T23:47:10.396260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:47:10.482719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
30.3%
hyundai 19
21.3%
heavy 19
21.3%
industries 19
21.3%
qd 2
 
2.2%
kawasaki 1
 
1.1%
sakaide 1
 
1.1%
works 1
 
1.1%

BULD_YR
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1798.2245
Minimum0
Maximum2019
Zeros4
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:10.571978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11900
median1993
Q32000
95-th percentile2009
Maximum2019
Range2019
Interquartile range (IQR)100

Descriptive statistics

Standard deviation543.8342
Coefficient of variation (CV)0.30242842
Kurtosis8.1039056
Mean1798.2245
Median Absolute Deviation (MAD)24
Skewness-3.1090005
Sum88113
Variance295755.64
MonotonicityNot monotonic
2023-12-10T23:47:10.696002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1900 19
38.8%
2001 5
 
10.2%
0 4
 
8.2%
1995 4
 
8.2%
1998 3
 
6.1%
2009 2
 
4.1%
2000 2
 
4.1%
1996 2
 
4.1%
1991 1
 
2.0%
2017 1
 
2.0%
Other values (6) 6
 
12.2%
ValueCountFrequency (%)
0 4
 
8.2%
1900 19
38.8%
1991 1
 
2.0%
1993 1
 
2.0%
1994 1
 
2.0%
1995 4
 
8.2%
1996 2
 
4.1%
1997 1
 
2.0%
1998 3
 
6.1%
2000 2
 
4.1%
ValueCountFrequency (%)
2019 1
 
2.0%
2017 1
 
2.0%
2009 2
 
4.1%
2008 1
 
2.0%
2005 1
 
2.0%
2001 5
10.2%
2000 2
 
4.1%
1998 3
6.1%
1997 1
 
2.0%
1996 2
 
4.1%

DDWGHT
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17827.571
Minimum0
Maximum311620
Zeros22
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:10.803358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median922
Q34999
95-th percentile82182
Maximum311620
Range311620
Interquartile range (IQR)4999

Descriptive statistics

Standard deviation62335.851
Coefficient of variation (CV)3.496598
Kurtosis18.657945
Mean17827.571
Median Absolute Deviation (MAD)922
Skewness4.3533652
Sum873551
Variance3.8857583 × 109
MonotonicityNot monotonic
2023-12-10T23:47:10.911070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 22
44.9%
4999 6
 
12.2%
670 1
 
2.0%
88668 1
 
2.0%
8823 1
 
2.0%
22354 1
 
2.0%
963 1
 
2.0%
5565 1
 
2.0%
1892 1
 
2.0%
2170 1
 
2.0%
Other values (13) 13
26.5%
ValueCountFrequency (%)
0 22
44.9%
670 1
 
2.0%
906 1
 
2.0%
922 1
 
2.0%
963 1
 
2.0%
1156 1
 
2.0%
1532 1
 
2.0%
1892 1
 
2.0%
1909 1
 
2.0%
2170 1
 
2.0%
ValueCountFrequency (%)
311620 1
 
2.0%
301661 1
 
2.0%
88668 1
 
2.0%
72453 1
 
2.0%
22354 1
 
2.0%
8823 1
 
2.0%
6729 1
 
2.0%
5565 1
 
2.0%
4999 6
12.2%
4907 1
 
2.0%

DPTR_HMS
Date

Distinct37
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:00
Maximum2023-01-01 00:02:37
2023-12-10T23:47:11.034951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:11.173827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)

ARVL_HMS
Date

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-07-27 00:40:00
Maximum2023-05-31 23:59:26
2023-12-10T23:47:11.306899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:11.404578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)

DPTRP_LA
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.123167
Minimum-1.26157
Maximum42.6544
Zeros6
Zeros (%)12.2%
Negative1
Negative (%)2.0%
Memory size573.0 B
2023-12-10T23:47:11.516361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.26157
5-th percentile0
Q132.982498
median34.111301
Q334.4207
95-th percentile35.542419
Maximum42.6544
Range43.91597
Interquartile range (IQR)1.438202

Descriptive statistics

Standard deviation13.869785
Coefficient of variation (CV)0.51136304
Kurtosis0.026395935
Mean27.123167
Median Absolute Deviation (MAD)0.886998
Skewness-1.3469098
Sum1329.0352
Variance192.37094
MonotonicityNot monotonic
2023-12-10T23:47:11.937736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 6
 
12.2%
33.148499 2
 
4.1%
34.207298 1
 
2.0%
42.6544 1
 
2.0%
-1.26157 1
 
2.0%
35.516201 1
 
2.0%
34.9552 1
 
2.0%
34.225899 1
 
2.0%
40.458199 1
 
2.0%
33.3494 1
 
2.0%
Other values (33) 33
67.3%
ValueCountFrequency (%)
-1.26157 1
 
2.0%
0.0 6
12.2%
0.423952 1
 
2.0%
5.51687 1
 
2.0%
5.68443 1
 
2.0%
13.2283 1
 
2.0%
25.927401 1
 
2.0%
32.982498 1
 
2.0%
32.982601 1
 
2.0%
33.148499 2
 
4.1%
ValueCountFrequency (%)
42.6544 1
2.0%
40.458199 1
2.0%
35.559898 1
2.0%
35.516201 1
2.0%
35.0163 1
2.0%
34.998402 1
2.0%
34.998299 1
2.0%
34.959702 1
2.0%
34.9552 1
2.0%
34.895 1
2.0%

DPTRP_LO
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.96039
Minimum0
Maximum175.972
Zeros6
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:12.053490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1131.065
median132.96899
Q3135.375
95-th percentile141.7272
Maximum175.972
Range175.972
Interquartile range (IQR)4.309998

Descriptive statistics

Standard deviation46.21104
Coefficient of variation (CV)0.39850711
Kurtosis2.5639831
Mean115.96039
Median Absolute Deviation (MAD)2.406006
Skewness-1.9476235
Sum5682.0592
Variance2135.4602
MonotonicityNot monotonic
2023-12-10T23:47:12.176947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 6
 
12.2%
136.656998 2
 
4.1%
131.824005 2
 
4.1%
131.897003 2
 
4.1%
133.382004 2
 
4.1%
133.779999 2
 
4.1%
129.725998 2
 
4.1%
133.121994 2
 
4.1%
135.375 1
 
2.0%
141.753998 1
 
2.0%
Other values (27) 27
55.1%
ValueCountFrequency (%)
0.0 6
12.2%
79.424301 1
 
2.0%
84.072197 1
 
2.0%
94.132698 1
 
2.0%
113.349998 1
 
2.0%
129.725998 2
 
4.1%
131.065002 1
 
2.0%
131.175003 1
 
2.0%
131.679993 1
 
2.0%
131.688004 1
 
2.0%
ValueCountFrequency (%)
175.972 1
2.0%
167.248993 1
2.0%
141.753998 1
2.0%
141.686996 1
2.0%
140.028 1
2.0%
139.837006 1
2.0%
139.742004 1
2.0%
136.682007 1
2.0%
136.671005 1
2.0%
136.656998 2
4.1%

DTNT_LA
Real number (ℝ)

ZEROS 

Distinct33
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.954938
Minimum-9.80191
Maximum42.652199
Zeros17
Zeros (%)34.7%
Negative1
Negative (%)2.0%
Memory size573.0 B
2023-12-10T23:47:12.295264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.80191
5-th percentile0
Q10
median33.148499
Q334.3116
95-th percentile35.531039
Maximum42.652199
Range52.454109
Interquartile range (IQR)34.3116

Descriptive statistics

Standard deviation17.476281
Coefficient of variation (CV)0.92199096
Kurtosis-1.9413035
Mean18.954938
Median Absolute Deviation (MAD)2.437801
Skewness-0.20894517
Sum928.79196
Variance305.42041
MonotonicityNot monotonic
2023-12-10T23:47:12.414446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 17
34.7%
34.3116 1
 
2.0%
2.9381 1
 
2.0%
1.58117 1
 
2.0%
35.5863 1
 
2.0%
33.950699 1
 
2.0%
34.2019 1
 
2.0%
34.542801 1
 
2.0%
34.152302 1
 
2.0%
35.522701 1
 
2.0%
Other values (23) 23
46.9%
ValueCountFrequency (%)
-9.80191 1
 
2.0%
0.0 17
34.7%
1.36577 1
 
2.0%
1.58117 1
 
2.0%
2.9381 1
 
2.0%
3.24333 1
 
2.0%
26.196699 1
 
2.0%
32.508301 1
 
2.0%
33.148499 1
 
2.0%
33.950699 1
 
2.0%
ValueCountFrequency (%)
42.652199 1
2.0%
35.5863 1
2.0%
35.536598 1
2.0%
35.522701 1
2.0%
35.516499 1
2.0%
35.494999 1
2.0%
35.483101 1
2.0%
34.560101 1
2.0%
34.542801 1
2.0%
34.4771 1
2.0%

DTNT_LO
Real number (ℝ)

ZEROS 

Distinct33
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.074912
Minimum-79.080002
Maximum172.93401
Zeros17
Zeros (%)34.7%
Negative1
Negative (%)2.0%
Memory size573.0 B
2023-12-10T23:47:12.536850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-79.080002
5-th percentile0
Q10
median131.79401
Q3134.519
95-th percentile140.08119
Maximum172.93401
Range252.01401
Interquartile range (IQR)134.519

Descriptive statistics

Standard deviation68.556833
Coefficient of variation (CV)0.83529584
Kurtosis-1.3459251
Mean82.074912
Median Absolute Deviation (MAD)8.26799
Skewness-0.59043182
Sum4021.6707
Variance4700.0393
MonotonicityNot monotonic
2023-12-10T23:47:12.666232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 17
34.7%
134.518997 1
 
2.0%
100.884003 1
 
2.0%
104.791 1
 
2.0%
140.061996 1
 
2.0%
136.513 1
 
2.0%
133.121994 1
 
2.0%
135.423004 1
 
2.0%
137.197006 1
 
2.0%
139.761002 1
 
2.0%
Other values (23) 23
46.9%
ValueCountFrequency (%)
-79.080002 1
 
2.0%
0.0 17
34.7%
77.187698 1
 
2.0%
100.884003 1
 
2.0%
104.791 1
 
2.0%
127.785004 1
 
2.0%
129.725998 1
 
2.0%
131.688004 1
 
2.0%
131.794006 1
 
2.0%
132.483994 1
 
2.0%
ValueCountFrequency (%)
172.934006 1
2.0%
141.679993 1
2.0%
140.093994 1
2.0%
140.061996 1
2.0%
140.028 1
2.0%
139.761002 1
2.0%
139.742004 1
2.0%
139.692993 1
2.0%
137.197006 1
2.0%
136.513 1
2.0%

MAX_VE
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.111571
Minimum0
Maximum102.3
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:12.805798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.5824
Q115.8134
median45.622
Q349.6647
95-th percentile102.3
Maximum102.3
Range102.3
Interquartile range (IQR)33.8513

Descriptive statistics

Standard deviation25.659811
Coefficient of variation (CV)0.62415057
Kurtosis1.1286824
Mean41.111571
Median Absolute Deviation (MAD)9.8744
Skewness1.0733876
Sum2014.467
Variance658.42589
MonotonicityNot monotonic
2023-12-10T23:47:12.970187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
102.3 5
 
10.2%
49.6647 1
 
2.0%
43.8742 1
 
2.0%
49.8209 1
 
2.0%
49.8278 1
 
2.0%
46.8428 1
 
2.0%
42.0508 1
 
2.0%
49.6507 1
 
2.0%
49.1594 1
 
2.0%
46.614 1
 
2.0%
Other values (35) 35
71.4%
ValueCountFrequency (%)
0.0 1
2.0%
12.4218 1
2.0%
13.4068 1
2.0%
13.8458 1
2.0%
14.339 1
2.0%
14.4813 1
2.0%
14.6475 1
2.0%
14.9818 1
2.0%
15.0296 1
2.0%
15.1497 1
2.0%
ValueCountFrequency (%)
102.3 5
10.2%
49.9506 1
 
2.0%
49.9497 1
 
2.0%
49.9117 1
 
2.0%
49.895 1
 
2.0%
49.8278 1
 
2.0%
49.8209 1
 
2.0%
49.7539 1
 
2.0%
49.6647 1
 
2.0%
49.6507 1
 
2.0%

AVE_VE
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.865148
Minimum0
Maximum37.1203
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:13.103624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.122242
Q110.2522
median12.2301
Q313.8578
95-th percentile28.77612
Maximum37.1203
Range37.1203
Interquartile range (IQR)3.6056

Descriptive statistics

Standard deviation6.7813138
Coefficient of variation (CV)0.4890906
Kurtosis3.851277
Mean13.865148
Median Absolute Deviation (MAD)1.9341
Skewness1.7594071
Sum679.39227
Variance45.986217
MonotonicityNot monotonic
2023-12-10T23:47:13.262906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
34.5489 1
 
2.0%
14.1642 1
 
2.0%
8.04559 1
 
2.0%
11.2669 1
 
2.0%
19.8221 1
 
2.0%
9.66083 1
 
2.0%
11.6813 1
 
2.0%
13.8578 1
 
2.0%
20.5879 1
 
2.0%
11.9529 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
7.46535 1
2.0%
8.04559 1
2.0%
8.23722 1
2.0%
8.4121 1
2.0%
8.63568 1
2.0%
8.9022 1
2.0%
9.09931 1
2.0%
9.66083 1
2.0%
9.9711 1
2.0%
ValueCountFrequency (%)
37.1203 1
2.0%
34.5489 1
2.0%
30.1644 1
2.0%
26.6937 1
2.0%
23.1635 1
2.0%
20.5879 1
2.0%
20.3467 1
2.0%
19.8221 1
2.0%
17.9427 1
2.0%
17.2116 1
2.0%

NVGTN_DIST
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36711540
Minimum0
Maximum1.27742 × 108
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:13.396155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1503608
Q112130000
median30099400
Q357907300
95-th percentile88928820
Maximum1.27742 × 108
Range1.27742 × 108
Interquartile range (IQR)45777300

Descriptive statistics

Standard deviation30523077
Coefficient of variation (CV)0.83143005
Kurtosis1.1492626
Mean36711540
Median Absolute Deviation (MAD)22090470
Skewness1.0236061
Sum1.7988654 × 109
Variance9.3165824 × 1014
MonotonicityNot monotonic
2023-12-10T23:47:13.537360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
42655900 1
 
2.0%
61389700 1
 
2.0%
8008930 1
 
2.0%
58515200 1
 
2.0%
66731100 1
 
2.0%
38625800 1
 
2.0%
57907300 1
 
2.0%
25034500 1
 
2.0%
62645800 1
 
2.0%
48128600 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0 1
2.0%
500606 1
2.0%
1171700 1
2.0%
2001470 1
2.0%
2348510 1
2.0%
2664060 1
2.0%
3272060 1
2.0%
3719420 1
2.0%
6724890 1
2.0%
6762120 1
2.0%
ValueCountFrequency (%)
127742000 1
2.0%
122562000 1
2.0%
97361700 1
2.0%
76279500 1
2.0%
68954900 1
2.0%
66731100 1
2.0%
64685800 1
2.0%
62645800 1
2.0%
62323200 1
2.0%
61389700 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
2023-12-10T23:47:13.667637image/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
2023-12-10T23:47:13.795515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

Sample

MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOMAX_VEAVE_VENVGTN_DISTRN
04310163969920289<NA><NA>0.00.00.0<NA>0.0HYUNDAI HEAVY INDUSTRIES1900006-Feb-2021 00:15:0031-Jul-2021 23:57:0034.207298133.1230010.00.049.664734.5489426559002
14310167199909390<NA><NA>0.00.00.0<NA>0.0HYUNDAI HEAVY INDUSTRIES1900024-Feb-2021 05:37:0031-Jul-2021 23:49:0033.155602131.82400535.522701139.76100235.747612.1491179304003
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424311009319242144KYOKUHOU MARUOil Products Tanker15.6104.810.0<NA>0.0<NA>2001556501-Jan-2021 00:01:0031-Jul-2021 23:58:0033.911701131.06500234.108398132.77400246.54212.73232659150044
434311009389233909SHOAN MARULPG Tanker11.264.520.0<NA>0.0<NA>200196301-Jan-2021 00:00:0031-Jul-2021 23:57:0035.0163139.8370060.00.049.266511.52243009940045
444311009669257541KYOKURYU MARUOil Products Tanker15.6104.470.0<NA>0.0<NA>2001499901-Jan-2021 00:14:0031-Jul-2021 23:57:0033.863899132.70700133.955502132.742996102.312.53824390100046
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476078203332081361IDLUFKHPWULXPSKTanker(chemical/oil product)24.6149.913.1QD9.3QD20172235401-Jan-2023 00:02:3731-May-2023 23:59:2613.2283113.3499983.24333-79.08000228.763612.2985064470049
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