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

Numeric12
Text1
Categorical7
Unsupported2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000465

Alerts

SHIP_HGHT has constant value ""Constant
DRAFT has constant value ""Constant
PRFMC has constant value ""Constant
FUEL_CNSMP_QTY has constant value ""Constant
DPTR_HMS is highly imbalanced (55.6%)Imbalance
ARVL_HMS is highly imbalanced (53.1%)Imbalance
SHIP_OWNER_NM has 49 (100.0%) missing valuesMissing
SHPYRD_NM has 49 (100.0%) missing valuesMissing
MMSI has unique valuesUnique
DPTRP_LA has unique valuesUnique
DTNT_LA has unique valuesUnique
DTNT_LO 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
BULD_YR has 2 (4.1%) zerosZeros
DDWGHT has 2 (4.1%) zerosZeros

Reproduction

Analysis started2023-12-10 14:32:02.655871
Analysis finished2023-12-10 14:32:02.841518
Duration0.19 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%
Mean4.3082044 × 108
Minimum4.220883 × 108
Maximum4.3100362 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:02.911828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.220883 × 108
5-th percentile4.3100134 × 108
Q14.3100176 × 108
median4.3100231 × 108
Q34.3100282 × 108
95-th percentile4.3100352 × 108
Maximum4.3100362 × 108
Range8915316
Interquartile range (IQR)1064

Descriptive statistics

Standard deviation1273437.7
Coefficient of variation (CV)0.0029558432
Kurtosis48.999963
Mean4.3082044 × 108
Median Absolute Deviation (MAD)551
Skewness-6.9999961
Sum2.1110202 × 1010
Variance1.6216435 × 1012
MonotonicityNot monotonic
2023-12-10T23:32:03.044839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
431003616 1
 
2.0%
431002607 1
 
2.0%
431001357 1
 
2.0%
431001343 1
 
2.0%
431001342 1
 
2.0%
431002547 1
 
2.0%
431002865 1
 
2.0%
431002821 1
 
2.0%
431002799 1
 
2.0%
431002774 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
422088300 1
2.0%
431000393 1
2.0%
431001342 1
2.0%
431001343 1
2.0%
431001357 1
2.0%
431001374 1
2.0%
431001387 1
2.0%
431001401 1
2.0%
431001417 1
2.0%
431001562 1
2.0%
ValueCountFrequency (%)
431003616 1
2.0%
431003614 1
2.0%
431003537 1
2.0%
431003497 1
2.0%
431003489 1
2.0%
431003469 1
2.0%
431003404 1
2.0%
431003382 1
2.0%
431003374 1
2.0%
431003365 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

ZEROS 

Distinct40
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7612293.2
Minimum0
Maximum9661780
Zeros10
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:03.163909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19523744
median9574274
Q39599042
95-th percentile9639948.6
Maximum9661780
Range9661780
Interquartile range (IQR)75298

Descriptive statistics

Standard deviation3897222.1
Coefficient of variation (CV)0.51196426
Kurtosis0.29905258
Mean7612293.2
Median Absolute Deviation (MAD)32413
Skewness-1.5103718
Sum3.7300237 × 108
Variance1.518834 × 1013
MonotonicityNot monotonic
2023-12-10T23:32:03.293530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 10
 
20.4%
9661780 1
 
2.0%
9614256 1
 
2.0%
9572587 1
 
2.0%
9554468 1
 
2.0%
9574274 1
 
2.0%
9575125 1
 
2.0%
9597460 1
 
2.0%
9606687 1
 
2.0%
9606481 1
 
2.0%
Other values (30) 30
61.2%
ValueCountFrequency (%)
0 10
20.4%
8610435 1
 
2.0%
9470715 1
 
2.0%
9523744 1
 
2.0%
9540560 1
 
2.0%
9540754 1
 
2.0%
9552654 1
 
2.0%
9554468 1
 
2.0%
9560455 1
 
2.0%
9560467 1
 
2.0%
ValueCountFrequency (%)
9661780 1
2.0%
9658537 1
2.0%
9643049 1
2.0%
9635298 1
2.0%
9634787 1
2.0%
9634608 1
2.0%
9634593 1
2.0%
9632674 1
2.0%
9614256 1
2.0%
9606687 1
2.0%

SHIP_NM
Text

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:32:03.475390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length12.44898
Min length3

Characters and Unicode

Total characters610
Distinct characters32
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

Unique47 ?
Unique (%)95.9%

Sample

1st rowSHINSHIN MARU
2nd rowEIWA MARU
3rd rowSHIOTA MARU NO.8
4th rowNANIWA MARU NO.48
5th rowKIYO MARU NO.2
ValueCountFrequency (%)
maru 43
37.4%
no.3 4
 
3.5%
no.5 3
 
2.6%
no.2 3
 
2.6%
no.8 2
 
1.7%
kinyo 2
 
1.7%
yuho 2
 
1.7%
yosho 1
 
0.9%
eika 1
 
0.9%
isoprene 1
 
0.9%
Other values (53) 53
46.1%
2023-12-10T23:32:03.826389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 74
12.1%
66
10.8%
U 64
10.5%
O 54
8.9%
R 52
 
8.5%
M 49
 
8.0%
N 37
 
6.1%
I 35
 
5.7%
K 28
 
4.6%
. 21
 
3.4%
Other values (22) 130
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 491
80.5%
Space Separator 66
 
10.8%
Decimal Number 32
 
5.2%
Other Punctuation 21
 
3.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 74
15.1%
U 64
13.0%
O 54
11.0%
R 52
10.6%
M 49
10.0%
N 37
7.5%
I 35
7.1%
K 28
 
5.7%
H 20
 
4.1%
S 19
 
3.9%
Other values (12) 59
12.0%
Decimal Number
ValueCountFrequency (%)
1 7
21.9%
8 5
15.6%
0 5
15.6%
3 5
15.6%
2 4
12.5%
5 4
12.5%
4 1
 
3.1%
6 1
 
3.1%
Space Separator
ValueCountFrequency (%)
66
100.0%
Other Punctuation
ValueCountFrequency (%)
. 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 491
80.5%
Common 119
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 74
15.1%
U 64
13.0%
O 54
11.0%
R 52
10.6%
M 49
10.0%
N 37
7.5%
I 35
7.1%
K 28
 
5.7%
H 20
 
4.1%
S 19
 
3.9%
Other values (12) 59
12.0%
Common
ValueCountFrequency (%)
66
55.5%
. 21
 
17.6%
1 7
 
5.9%
8 5
 
4.2%
0 5
 
4.2%
3 5
 
4.2%
2 4
 
3.4%
5 4
 
3.4%
4 1
 
0.8%
6 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 74
12.1%
66
10.8%
U 64
10.5%
O 54
8.9%
R 52
 
8.5%
M 49
 
8.0%
N 37
 
6.1%
I 35
 
5.7%
K 28
 
4.6%
. 21
 
3.4%
Other values (22) 130
21.3%

SHIP_KIND
Categorical

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
Oil Products Tanker
17 
Chemical Tanker
12 
LPG Tanker
11 
Oil or Chemical Tanker
6 
Tanker
2 

Length

Max length22
Median length19
Mean length15.77551
Min length6

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowOil or Chemical Tanker
2nd rowOil or Chemical Tanker
3rd rowOil Products Tanker
4th rowOil Products Tanker
5th rowOil Products Tanker

Common Values

ValueCountFrequency (%)
Oil Products Tanker 17
34.7%
Chemical Tanker 12
24.5%
LPG Tanker 11
22.4%
Oil or Chemical Tanker 6
 
12.2%
Tanker 2
 
4.1%
Bunkering Tanker 1
 
2.0%

Length

2023-12-10T23:32:03.952550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:04.072093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
tanker 49
39.2%
oil 23
18.4%
chemical 18
 
14.4%
products 17
 
13.6%
lpg 11
 
8.8%
or 6
 
4.8%
bunkering 1
 
0.8%

SHIP_WDTH
Real number (ℝ)

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.502449
Minimum7
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:04.201920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9
Q110
median11.5
Q311.6
95-th percentile16
Maximum16
Range9
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation2.3529685
Coefficient of variation (CV)0.20456239
Kurtosis-0.01642108
Mean11.502449
Median Absolute Deviation (MAD)1.5
Skewness0.82835257
Sum563.62
Variance5.5364605
MonotonicityNot monotonic
2023-12-10T23:32:04.306820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11.5 14
28.6%
10.0 13
26.5%
16.0 6
12.2%
9.0 4
 
8.2%
15.5 2
 
4.1%
12.0 2
 
4.1%
11.6 1
 
2.0%
11.4 1
 
2.0%
9.02 1
 
2.0%
11.8 1
 
2.0%
Other values (4) 4
 
8.2%
ValueCountFrequency (%)
7.0 1
 
2.0%
8.0 1
 
2.0%
9.0 4
 
8.2%
9.02 1
 
2.0%
10.0 13
26.5%
11.0 1
 
2.0%
11.4 1
 
2.0%
11.5 14
28.6%
11.6 1
 
2.0%
11.8 1
 
2.0%
ValueCountFrequency (%)
16.0 6
12.2%
15.8 1
 
2.0%
15.5 2
 
4.1%
12.0 2
 
4.1%
11.8 1
 
2.0%
11.6 1
 
2.0%
11.5 14
28.6%
11.4 1
 
2.0%
11.0 1
 
2.0%
10.0 13
26.5%

SHIP_LNTH
Real number (ℝ)

Distinct38
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.708776
Minimum36.59
Maximum117.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:04.404302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.59
5-th percentile44.78
Q164.5
median67.8
Q373.22
95-th percentile104.902
Maximum117.55
Range80.96
Interquartile range (IQR)8.72

Descriptive statistics

Standard deviation18.56718
Coefficient of variation (CV)0.2589248
Kurtosis0.40828698
Mean71.708776
Median Absolute Deviation (MAD)4.15
Skewness0.76985351
Sum3513.73
Variance344.74018
MonotonicityNot monotonic
2023-12-10T23:32:04.521055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
67.8 5
 
10.2%
64.99 3
 
6.1%
103.9 2
 
4.1%
64.46 2
 
4.1%
67.9 2
 
4.1%
67.0 2
 
4.1%
64.5 2
 
4.1%
74.15 1
 
2.0%
74.96 1
 
2.0%
52.45 1
 
2.0%
Other values (28) 28
57.1%
ValueCountFrequency (%)
36.59 1
2.0%
37.0 1
2.0%
40.0 1
2.0%
51.95 1
2.0%
52.45 1
2.0%
52.9 1
2.0%
54.72 1
2.0%
59.5 1
2.0%
62.72 1
2.0%
64.46 2
4.1%
ValueCountFrequency (%)
117.55 1
2.0%
104.99 1
2.0%
104.97 1
2.0%
104.8 1
2.0%
104.45 1
2.0%
104.39 1
2.0%
104.12 1
2.0%
103.9 2
4.1%
78.6 1
2.0%
74.96 1
2.0%

SHIP_HGHT
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

2023-12-10T23:32:04.631481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:04.712836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

SHIP_OWNER_NM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

DRAFT
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

2023-12-10T23:32:04.805938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:04.885654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

SHPYRD_NM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

BULD_YR
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1927.7347
Minimum0
Maximum2012
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:04.956548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1990
Q12010
median2010
Q32011
95-th percentile2012
Maximum2012
Range2012
Interquartile range (IQR)1

Descriptive statistics

Standard deviation401.80394
Coefficient of variation (CV)0.20843322
Kurtosis21.821696
Mean1927.7347
Median Absolute Deviation (MAD)1
Skewness-4.7884314
Sum94459
Variance161446.41
MonotonicityNot monotonic
2023-12-10T23:32:05.062564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2010 20
40.8%
2011 13
26.5%
2012 10
20.4%
0 2
 
4.1%
1993 1
 
2.0%
2007 1
 
2.0%
2008 1
 
2.0%
1988 1
 
2.0%
ValueCountFrequency (%)
0 2
 
4.1%
1988 1
 
2.0%
1993 1
 
2.0%
2007 1
 
2.0%
2008 1
 
2.0%
2010 20
40.8%
2011 13
26.5%
2012 10
20.4%
ValueCountFrequency (%)
2012 10
20.4%
2011 13
26.5%
2010 20
40.8%
2008 1
 
2.0%
2007 1
 
2.0%
1993 1
 
2.0%
1988 1
 
2.0%
0 2
 
4.1%

DDWGHT
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1956.9592
Minimum0
Maximum6058
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:05.471559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile435.2
Q11011
median1231
Q31787
95-th percentile5567.6
Maximum6058
Range6058
Interquartile range (IQR)776

Descriptive statistics

Standard deviation1778.3153
Coefficient of variation (CV)0.90871352
Kurtosis0.63535052
Mean1956.9592
Median Absolute Deviation (MAD)257
Skewness1.4840611
Sum95891
Variance3162405.2
MonotonicityNot monotonic
2023-12-10T23:32:05.620378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 2
 
4.1%
1180 2
 
4.1%
1200 2
 
4.1%
999 2
 
4.1%
660 2
 
4.1%
1794 1
 
2.0%
1284 1
 
2.0%
1265 1
 
2.0%
974 1
 
2.0%
1079 1
 
2.0%
Other values (34) 34
69.4%
ValueCountFrequency (%)
0 2
4.1%
326 1
2.0%
599 1
2.0%
660 2
4.1%
696 1
2.0%
974 1
2.0%
979 1
2.0%
982 1
2.0%
999 2
4.1%
1011 1
2.0%
ValueCountFrequency (%)
6058 1
2.0%
5891 1
2.0%
5584 1
2.0%
5543 1
2.0%
5533 1
2.0%
5486 1
2.0%
5479 1
2.0%
5422 1
2.0%
4999 1
2.0%
2301 1
2.0%

DPTR_HMS
Categorical

IMBALANCE 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2021 00:00:00
39 
02-Jan-2021 00:00:00
4 
03-Jan-2021 00:00:00
 
3
07-Jan-2021 00:00:00
 
1
04-Jan-2021 00:00:00
 
1

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique3 ?
Unique (%)6.1%

Sample

1st row01-Jan-2021 00:00:00
2nd row01-Jan-2021 00:00:00
3rd row02-Jan-2021 00:00:00
4th row01-Jan-2021 00:00:00
5th row01-Jan-2021 00:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2021 00:00:00 39
79.6%
02-Jan-2021 00:00:00 4
 
8.2%
03-Jan-2021 00:00:00 3
 
6.1%
07-Jan-2021 00:00:00 1
 
2.0%
04-Jan-2021 00:00:00 1
 
2.0%
11-Jan-2021 00:00:00 1
 
2.0%

Length

2023-12-10T23:32:05.754219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:05.844969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 49
50.0%
01-jan-2021 39
39.8%
02-jan-2021 4
 
4.1%
03-jan-2021 3
 
3.1%
07-jan-2021 1
 
1.0%
04-jan-2021 1
 
1.0%
11-jan-2021 1
 
1.0%

ARVL_HMS
Categorical

IMBALANCE 

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
13-Oct-2021 18:00:00
37 
13-Oct-2021 12:00:00
 
3
13-Oct-2021 06:00:00
 
3
24-Aug-2021 00:00:00
 
1
12-Oct-2021 18:00:00
 
1
Other values (4)
4 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique6 ?
Unique (%)12.2%

Sample

1st row13-Oct-2021 18:00:00
2nd row13-Oct-2021 18:00:00
3rd row13-Oct-2021 18:00:00
4th row13-Oct-2021 18:00:00
5th row13-Oct-2021 18:00:00

Common Values

ValueCountFrequency (%)
13-Oct-2021 18:00:00 37
75.5%
13-Oct-2021 12:00:00 3
 
6.1%
13-Oct-2021 06:00:00 3
 
6.1%
24-Aug-2021 00:00:00 1
 
2.0%
12-Oct-2021 18:00:00 1
 
2.0%
12-Oct-2021 06:00:00 1
 
2.0%
11-Oct-2021 00:00:00 1
 
2.0%
12-Sep-2021 18:00:00 1
 
2.0%
12-Oct-2021 12:00:00 1
 
2.0%

Length

2023-12-10T23:32:05.977414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:06.195074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13-oct-2021 43
43.9%
18:00:00 39
39.8%
12:00:00 4
 
4.1%
06:00:00 4
 
4.1%
12-oct-2021 3
 
3.1%
00:00:00 2
 
2.0%
24-aug-2021 1
 
1.0%
11-oct-2021 1
 
1.0%
12-sep-2021 1
 
1.0%

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.975555
Minimum24.683901
Maximum39.766701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:06.674096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.683901
5-th percentile28.70168
Q133.7589
median34.4352
Q335.2952
95-th percentile37.310858
Maximum39.766701
Range15.0828
Interquartile range (IQR)1.5363

Descriptive statistics

Standard deviation2.7281177
Coefficient of variation (CV)0.080296486
Kurtosis3.0260505
Mean33.975555
Median Absolute Deviation (MAD)0.7109
Skewness-1.1740817
Sum1664.8022
Variance7.4426261
MonotonicityNot monotonic
2023-12-10T23:32:07.059893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
34.436798 1
 
2.0%
35.513401 1
 
2.0%
39.756901 1
 
2.0%
34.8918 1
 
2.0%
34.3829 1
 
2.0%
29.438101 1
 
2.0%
34.473701 1
 
2.0%
34.008999 1
 
2.0%
33.7589 1
 
2.0%
35.0126 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
24.683901 1
2.0%
27.0954 1
2.0%
28.2698 1
2.0%
29.349501 1
2.0%
29.438101 1
2.0%
30.625401 1
2.0%
30.802 1
2.0%
30.8286 1
2.0%
33.283699 1
2.0%
33.623199 1
2.0%
ValueCountFrequency (%)
39.766701 1
2.0%
39.756901 1
2.0%
38.235298 1
2.0%
35.924198 1
2.0%
35.589199 1
2.0%
35.571098 1
2.0%
35.537899 1
2.0%
35.532398 1
2.0%
35.525501 1
2.0%
35.513401 1
2.0%

DPTRP_LO
Real number (ℝ)

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.75805
Minimum56.147301
Maximum141.07899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:07.311351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56.147301
5-th percentile118.2634
Q1132.097
median133.78799
Q3139.66701
95-th percentile140.056
Maximum141.07899
Range84.931694
Interquartile range (IQR)7.570007

Descriptive statistics

Standard deviation13.010004
Coefficient of variation (CV)0.098741626
Kurtosis23.99903
Mean131.75805
Median Absolute Deviation (MAD)3.081009
Skewness-4.3531186
Sum6456.1443
Variance169.2602
MonotonicityNot monotonic
2023-12-10T23:32:07.463633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
140.052002 2
 
4.1%
140.056 2
 
4.1%
133.544006 1
 
2.0%
132.488998 1
 
2.0%
133.164001 1
 
2.0%
114.346001 1
 
2.0%
133.755997 1
 
2.0%
131.783005 1
 
2.0%
132.697998 1
 
2.0%
138.494995 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
56.147301 1
2.0%
114.346001 1
2.0%
118.004997 1
2.0%
118.651001 1
2.0%
118.949997 1
2.0%
119.209999 1
2.0%
119.221001 1
2.0%
120.388 1
2.0%
130.358002 1
2.0%
131.656006 1
2.0%
ValueCountFrequency (%)
141.078995 1
2.0%
140.709 1
2.0%
140.056 2
4.1%
140.052002 2
4.1%
140.046005 1
2.0%
140.037003 1
2.0%
139.865997 1
2.0%
139.781006 1
2.0%
139.710999 1
2.0%
139.688004 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.73959
Minimum26.569901
Maximum42.357498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:07.652083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.569901
5-th percentile33.287601
Q134.047199
median34.441002
Q334.942101
95-th percentile39.706859
Maximum42.357498
Range15.787597
Interquartile range (IQR)0.894902

Descriptive statistics

Standard deviation2.2773875
Coefficient of variation (CV)0.06555597
Kurtosis6.8781751
Mean34.73959
Median Absolute Deviation (MAD)0.496101
Skewness0.77396351
Sum1702.2399
Variance5.1864939
MonotonicityNot monotonic
2023-12-10T23:32:07.810748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
34.435902 1
 
2.0%
35.753601 1
 
2.0%
38.181 1
 
2.0%
34.587002 1
 
2.0%
35.571999 1
 
2.0%
33.3428 1
 
2.0%
34.441002 1
 
2.0%
33.345299 1
 
2.0%
33.896599 1
 
2.0%
34.455101 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
26.569901 1
2.0%
31.667601 1
2.0%
33.250801 1
2.0%
33.3428 1
2.0%
33.345299 1
2.0%
33.434502 1
2.0%
33.4618 1
2.0%
33.611099 1
2.0%
33.896599 1
2.0%
33.906799 1
2.0%
ValueCountFrequency (%)
42.357498 1
2.0%
41.524799 1
2.0%
40.724098 1
2.0%
38.181 1
2.0%
35.753601 1
2.0%
35.717499 1
2.0%
35.571999 1
2.0%
35.486599 1
2.0%
35.482399 1
2.0%
35.471001 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.71774
Minimum54.001499
Maximum141.25301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:07.996283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54.001499
5-th percentile130.2606
Q1132.23801
median135.327
Q3137.69701
95-th percentile140.86639
Maximum141.25301
Range87.251507
Interquartile range (IQR)5.458999

Descriptive statistics

Standard deviation12.09437
Coefficient of variation (CV)0.090447008
Kurtosis41.385311
Mean133.71774
Median Absolute Deviation (MAD)3.020996
Skewness-6.1803709
Sum6552.1695
Variance146.27378
MonotonicityNot monotonic
2023-12-10T23:32:08.162968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
137.697006 1
 
2.0%
140.854996 1
 
2.0%
139.410004 1
 
2.0%
135.384995 1
 
2.0%
140.044006 1
 
2.0%
129.815002 1
 
2.0%
133.664001 1
 
2.0%
129.817001 1
 
2.0%
131.173996 1
 
2.0%
133.664993 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
54.001499 1
2.0%
129.815002 1
2.0%
129.817001 1
2.0%
130.925995 1
2.0%
131.112 1
2.0%
131.169998 1
2.0%
131.173996 1
2.0%
131.287003 1
2.0%
131.567001 1
2.0%
131.602997 1
2.0%
ValueCountFrequency (%)
141.253006 1
2.0%
140.929993 1
2.0%
140.873993 1
2.0%
140.854996 1
2.0%
140.044006 1
2.0%
139.927994 1
2.0%
139.910995 1
2.0%
139.832993 1
2.0%
139.671997 1
2.0%
139.410004 1
2.0%

PRFMC
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

2023-12-10T23:32:08.343963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:08.441624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

FUEL_CNSMP_QTY
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

2023-12-10T23:32:08.526459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:08.634517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

NVGTN_DIST
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49373.777
Minimum770.53
Maximum77172.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:08.738529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum770.53
5-th percentile9891.986
Q141823.8
median55229.8
Q363424.4
95-th percentile72619.12
Maximum77172.5
Range76401.97
Interquartile range (IQR)21600.6

Descriptive statistics

Standard deviation19159.723
Coefficient of variation (CV)0.38805463
Kurtosis0.268632
Mean49373.777
Median Absolute Deviation (MAD)9920.8
Skewness-0.99154829
Sum2419315.1
Variance3.6709499 × 108
MonotonicityNot monotonic
2023-12-10T23:32:08.901121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
55229.8 1
 
2.0%
45426.9 1
 
2.0%
26903.3 1
 
2.0%
58392.3 1
 
2.0%
64623.9 1
 
2.0%
43516.6 1
 
2.0%
50602.6 1
 
2.0%
55884.7 1
 
2.0%
59513.9 1
 
2.0%
52221.5 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
770.53 1
2.0%
7263.24 1
2.0%
8617.71 1
2.0%
11803.4 1
2.0%
12962.5 1
2.0%
14839.2 1
2.0%
24493.3 1
2.0%
26903.3 1
2.0%
33427.2 1
2.0%
38989.2 1
2.0%
ValueCountFrequency (%)
77172.5 1
2.0%
74432.5 1
2.0%
72969.4 1
2.0%
72093.7 1
2.0%
69449.3 1
2.0%
68499.7 1
2.0%
68113.2 1
2.0%
65204.3 1
2.0%
64623.9 1
2.0%
63847.0 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
2023-12-10T23:32:09.065173image/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
2023-12-10T23:32:09.208845image/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
04310036169661780SHINSHIN MARUOil or Chemical Tanker11.574.150<NA>0<NA>2012179401-Jan-2021 00:00:0013-Oct-2021 18:00:0034.436798133.54400634.435902137.6970060055229.82
14310036149643049EIWA MARUOil or Chemical Tanker10.064.970<NA>0<NA>2012123101-Jan-2021 00:00:0013-Oct-2021 18:00:0035.395599139.71099934.166801132.3060055272.03
24310028970SHIOTA MARU NO.8Oil Products Tanker10.064.990<NA>0<NA>2011118002-Jan-2021 00:00:0013-Oct-2021 18:00:0034.4352134.91499334.603199137.2559970039063.64
34310035379634787NANIWA MARU NO.48Oil Products Tanker16.0104.450<NA>0<NA>2012589101-Jan-2021 00:00:0013-Oct-2021 18:00:0035.537899140.04600534.942101136.6399990061396.25
44310034979634608KIYO MARU NO.2Oil Products Tanker15.5103.90<NA>0<NA>2012542201-Jan-2021 00:00:0013-Oct-2021 18:00:0034.135101132.40600642.357498140.9299930058652.76
54310034899658537RIEI MARUOil Products Tanker11.578.60<NA>0<NA>2012230101-Jan-2021 00:00:0013-Oct-2021 18:00:0034.507099133.67500333.935101131.1120024493.37
64310034690KIIOil Products Tanker10.064.990<NA>0<NA>2012118001-Jan-2021 00:00:0013-Oct-2021 18:00:0035.2952139.68800434.4842135.3699950041823.88
74310034049635298YUHO MARUOil Products Tanker16.0104.80<NA>0<NA>2012548601-Jan-2021 00:00:0024-Aug-2021 00:00:0034.220501132.27200334.4007133.18800400770.539
84310033820SEIYOH 101Chemical Tanker10.064.460<NA>0<NA>2012125401-Jan-2021 00:00:0013-Oct-2021 18:00:0034.297798133.78799433.4618135.7350010061498.910
94310033749634593MATSU MARU NO.5Oil Products Tanker15.5103.90<NA>0<NA>2012547901-Jan-2021 00:00:0013-Oct-2021 18:00:0034.492901133.74499533.611099136.4669950069449.311
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
394310003939470715NADESHIKO MARUOil or Chemical Tanker11.574.960<NA>0<NA>2007190601-Jan-2021 00:00:0013-Oct-2021 18:00:0033.6301130.35800233.944901130.9259950057550.241
404310025089597965YUHO MARULPG Tanker11.567.80<NA>0<NA>2011108101-Jan-2021 00:00:0013-Oct-2021 06:00:0035.525501139.86599734.611136.5610050063723.742
414310024670ENEOS LUBEBunkering Tanker9.036.590<NA>0<NA>200832607-Jan-2021 00:00:0013-Oct-2021 06:00:0035.470699139.66700735.482399139.671997007263.2443
424310023840SYOUHAKU MARUTanker7.037.00<NA>0<NA>0004-Jan-2021 00:00:0013-Oct-2021 12:00:0034.354599133.13200434.205898133.1210020012962.544
434310023760HOTOKU MARUChemical Tanker10.064.470<NA>0<NA>2011120802-Jan-2021 00:00:0013-Oct-2021 18:00:0034.038601131.79600534.936001136.7740020065204.345
444310023149540754L.P.MARU NO.8LPG Tanker11.567.90<NA>0<NA>201199901-Jan-2021 00:00:0013-Oct-2021 18:00:0029.349501118.65100134.963402136.6569980038989.246
454310023139591117HARUEI MARUOil Products Tanker12.072.20<NA>0<NA>2011178701-Jan-2021 00:00:0013-Oct-2021 18:00:0028.2698118.00499731.667601131.5670010043598.147
464310022619573878MIYA MARU NO.1Oil Products Tanker16.0104.990<NA>0<NA>2011558401-Jan-2021 00:00:0013-Oct-2021 18:00:0030.625401119.22100141.524799141.2530060074432.548
474310022019597329RYUSHO MARU NO.5Oil Products Tanker9.054.720<NA>0<NA>201059901-Jan-2021 00:00:0013-Oct-2021 12:00:0033.7243132.09734.1894132.2359920033427.249
484220883008610435FADAK 6000Chemical Tanker15.8117.550<NA>0<NA>1988605811-Jan-2021 00:00:0012-Oct-2021 12:00:0027.095456.14730126.56990154.001499008617.7150