Overview

Dataset statistics

Number of variables8
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory71.7 B

Variable types

Text1
Numeric5
Categorical2

Dataset

DescriptionSample
Author올시데이터
URLhttp://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001331

Alerts

SHIP_CNT is highly overall correlated with FUEL_CNSMP_QTY and 1 other fieldsHigh correlation
PRFMC is highly overall correlated with RNHigh correlation
FUEL_CNSMP_QTY is highly overall correlated with SHIP_CNT and 1 other fieldsHigh correlation
NVGTN_DIST is highly overall correlated with SHIP_CNT and 1 other fieldsHigh correlation
RN is highly overall correlated with PRFMCHigh correlation
DPTR_HMS is highly overall correlated with ARVL_HMSHigh correlation
ARVL_HMS is highly overall correlated with DPTR_HMSHigh correlation
DPTR_HMS is highly imbalanced (64.8%)Imbalance
ARVL_HMS is highly imbalanced (69.2%)Imbalance
SHIP_OWNER_NM has unique valuesUnique
PRFMC has unique valuesUnique
FUEL_CNSMP_QTY has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-09-25 08:57:28.289156
Analysis finished2023-09-25 08:57:35.371549
Duration7.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHIP_OWNER_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-09-25T17:57:35.706568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length14.346939
Min length4

Characters and Unicode

Total characters703
Distinct characters48
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st rowMdfnvrrqVklsslqj
2nd rowQruvhVklskroglqj
3rd rowOdqwdxVklsslqjFr
4th rowFrvprvklsPdqdjhphqw
5th rowWudqvzruogJurxs
ValueCountFrequency (%)
mdfnvrrqvklsslqj 1
 
2.0%
kduerxuolqnpdulqh 1
 
2.0%
gdqcxqgwlhwmhqv 1
 
2.0%
frqwvklsvpdqdjhphqw 1
 
2.0%
wyopdulqh 1
 
2.0%
phlkrndlxq 1
 
2.0%
zerfnvwlhjho 1
 
2.0%
zlvgrppdulqhjurxs 1
 
2.0%
qdqoldqvklspjpw 1
 
2.0%
edofrqwdlqhuolqh 1
 
2.0%
Other values (39) 39
79.6%
2023-09-25T17:57:36.495134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 75
 
10.7%
q 70
 
10.0%
d 68
 
9.7%
h 51
 
7.3%
u 35
 
5.0%
j 34
 
4.8%
r 33
 
4.7%
s 32
 
4.6%
v 29
 
4.1%
k 29
 
4.1%
Other values (38) 247
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 569
80.9%
Uppercase Letter 134
 
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 75
13.2%
q 70
12.3%
d 68
12.0%
h 51
9.0%
u 35
 
6.2%
j 34
 
6.0%
r 33
 
5.8%
s 32
 
5.6%
v 29
 
5.1%
k 29
 
5.1%
Other values (14) 113
19.9%
Uppercase Letter
ValueCountFrequency (%)
V 26
19.4%
O 15
11.2%
P 13
9.7%
F 10
 
7.5%
N 9
 
6.7%
S 8
 
6.0%
K 7
 
5.2%
J 6
 
4.5%
R 6
 
4.5%
W 5
 
3.7%
Other values (14) 29
21.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 703
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 75
 
10.7%
q 70
 
10.0%
d 68
 
9.7%
h 51
 
7.3%
u 35
 
5.0%
j 34
 
4.8%
r 33
 
4.7%
s 32
 
4.6%
v 29
 
4.1%
k 29
 
4.1%
Other values (38) 247
35.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 75
 
10.7%
q 70
 
10.0%
d 68
 
9.7%
h 51
 
7.3%
u 35
 
5.0%
j 34
 
4.8%
r 33
 
4.7%
s 32
 
4.6%
v 29
 
4.1%
k 29
 
4.1%
Other values (38) 247
35.1%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6326531
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:57:36.706074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8.6
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.4939491
Coefficient of variation (CV)1.2370983
Kurtosis21.367168
Mean3.6326531
Median Absolute Deviation (MAD)1
Skewness4.1188117
Sum178
Variance20.195578
MonotonicityNot monotonic
2023-09-25T17:57:36.903979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 15
30.6%
2 13
26.5%
3 5
 
10.2%
4 4
 
8.2%
5 4
 
8.2%
8 2
 
4.1%
6 2
 
4.1%
13 1
 
2.0%
29 1
 
2.0%
9 1
 
2.0%
ValueCountFrequency (%)
1 15
30.6%
2 13
26.5%
3 5
 
10.2%
4 4
 
8.2%
5 4
 
8.2%
6 2
 
4.1%
7 1
 
2.0%
8 2
 
4.1%
9 1
 
2.0%
13 1
 
2.0%
ValueCountFrequency (%)
29 1
 
2.0%
13 1
 
2.0%
9 1
 
2.0%
8 2
 
4.1%
7 1
 
2.0%
6 2
 
4.1%
5 4
 
8.2%
4 4
 
8.2%
3 5
 
10.2%
2 13
26.5%

DPTR_HMS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2023 12:00:00
42 
01-Apr-2023 12:00:00
 
2
01-Jan-2023 18:00:00
 
2
02-Jan-2023 00:00:00
 
1
03-Jan-2023 00:00:00
 
1

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique3 ?
Unique (%)6.1%

Sample

1st row01-Jan-2023 12:00:00
2nd row01-Jan-2023 12:00:00
3rd row01-Jan-2023 12:00:00
4th row01-Jan-2023 12:00:00
5th row01-Apr-2023 12:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2023 12:00:00 42
85.7%
01-Apr-2023 12:00:00 2
 
4.1%
01-Jan-2023 18:00:00 2
 
4.1%
02-Jan-2023 00:00:00 1
 
2.0%
03-Jan-2023 00:00:00 1
 
2.0%
03-Jan-2023 06:00:00 1
 
2.0%

Length

2023-09-25T17:57:37.128444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-25T17:57:37.389146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
01-jan-2023 44
44.9%
12:00:00 44
44.9%
01-apr-2023 2
 
2.0%
18:00:00 2
 
2.0%
00:00:00 2
 
2.0%
03-jan-2023 2
 
2.0%
02-jan-2023 1
 
1.0%
06:00:00 1
 
1.0%

ARVL_HMS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
30-Apr-2023 18:00:00
44 
30-Apr-2023 12:00:00
 
3
30-Apr-2023 06:00:00
 
1
29-Apr-2023 18:00:00
 
1

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique2 ?
Unique (%)4.1%

Sample

1st row30-Apr-2023 18:00:00
2nd row30-Apr-2023 18:00:00
3rd row30-Apr-2023 18:00:00
4th row30-Apr-2023 18:00:00
5th row30-Apr-2023 18:00:00

Common Values

ValueCountFrequency (%)
30-Apr-2023 18:00:00 44
89.8%
30-Apr-2023 12:00:00 3
 
6.1%
30-Apr-2023 06:00:00 1
 
2.0%
29-Apr-2023 18:00:00 1
 
2.0%

Length

2023-09-25T17:57:37.695207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-25T17:57:37.881254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30-apr-2023 48
49.0%
18:00:00 45
45.9%
12:00:00 3
 
3.1%
06:00:00 1
 
1.0%
29-apr-2023 1
 
1.0%

PRFMC
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89876543
Minimum62755500
Maximum98804200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:57:38.103568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62755500
5-th percentile73730140
Q186911600
median91363200
Q396008700
95-th percentile98601900
Maximum98804200
Range36048700
Interquartile range (IQR)9097100

Descriptive statistics

Standard deviation7943139.2
Coefficient of variation (CV)0.088378334
Kurtosis2.0391277
Mean89876543
Median Absolute Deviation (MAD)4645500
Skewness-1.3988107
Sum4.4039506 × 109
Variance6.309346 × 1013
MonotonicityStrictly increasing
2023-09-25T17:57:38.414731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
62755500 1
 
2.0%
96066100 1
 
2.0%
92882300 1
 
2.0%
92969800 1
 
2.0%
93038600 1
 
2.0%
93339700 1
 
2.0%
93485300 1
 
2.0%
93988300 1
 
2.0%
94418600 1
 
2.0%
94743700 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
62755500 1
2.0%
71655000 1
2.0%
73651900 1
2.0%
73847500 1
2.0%
77119700 1
2.0%
80734500 1
2.0%
81442800 1
2.0%
82447700 1
2.0%
83980100 1
2.0%
84224700 1
2.0%
ValueCountFrequency (%)
98804200 1
2.0%
98708000 1
2.0%
98634100 1
2.0%
98553600 1
2.0%
97814600 1
2.0%
97774700 1
2.0%
97682900 1
2.0%
97332300 1
2.0%
97303300 1
2.0%
96561000 1
2.0%

FUEL_CNSMP_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4128583 × 109
Minimum3.75722 × 108
Maximum3.00257 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:57:38.711769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.75722 × 108
5-th percentile1.000936 × 109
Q11.43252 × 109
median2.88607 × 109
Q35.5317 × 109
95-th percentile1.182462 × 1010
Maximum3.00257 × 1010
Range2.9649978 × 1010
Interquartile range (IQR)4.09918 × 109

Descriptive statistics

Standard deviation5.0364695 × 109
Coefficient of variation (CV)1.1413168
Kurtosis13.743702
Mean4.4128583 × 109
Median Absolute Deviation (MAD)1.72355 × 109
Skewness3.2429356
Sum2.1623006 × 1011
Variance2.5366025 × 1019
MonotonicityNot monotonic
2023-09-25T17:57:38.977053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2174990000 1
 
2.0%
2886070000 1
 
2.0%
1909770000 1
 
2.0%
30025700000 1
 
2.0%
2542120000 1
 
2.0%
992780000 1
 
2.0%
1079980000 1
 
2.0%
1104090000 1
 
2.0%
1102280000 1
 
2.0%
3885380000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
375722000 1
2.0%
909224000 1
2.0%
992780000 1
2.0%
1013170000 1
2.0%
1056480000 1
2.0%
1079980000 1
2.0%
1102280000 1
2.0%
1104090000 1
2.0%
1142350000 1
2.0%
1162520000 1
2.0%
ValueCountFrequency (%)
30025700000 1
2.0%
16581200000 1
2.0%
12460900000 1
2.0%
10870200000 1
2.0%
9323100000 1
2.0%
8807320000 1
2.0%
8504970000 1
2.0%
7647480000 1
2.0%
6901460000 1
2.0%
6454350000 1
2.0%

NVGTN_DIST
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.439093
Minimum4.87193
Maximum322.962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:57:39.308427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.87193
5-th percentile10.76912
Q116.8441
median31.7285
Q361.4917
95-th percentile120.9478
Maximum322.962
Range318.09007
Interquartile range (IQR)44.6476

Descriptive statistics

Standard deviation54.392594
Coefficient of variation (CV)1.1229069
Kurtosis13.683437
Mean48.439093
Median Absolute Deviation (MAD)18.1111
Skewness3.2664553
Sum2373.5156
Variance2958.5543
MonotonicityNot monotonic
2023-09-25T17:57:39.634715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
34.6581 1
 
2.0%
30.0425 1
 
2.0%
20.5612 1
 
2.0%
322.962 1
 
2.0%
27.3233 1
 
2.0%
10.6362 1
 
2.0%
11.5524 1
 
2.0%
11.7471 1
 
2.0%
11.6744 1
 
2.0%
41.0094 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
4.87193 1
2.0%
9.95175 1
2.0%
10.6362 1
2.0%
10.9685 1
2.0%
11.5524 1
2.0%
11.6744 1
2.0%
11.7471 1
2.0%
12.1558 1
2.0%
12.6253 1
2.0%
13.6174 1
2.0%
ValueCountFrequency (%)
322.962 1
2.0%
196.526 1
2.0%
127.393 1
2.0%
111.28 1
2.0%
96.7269 1
2.0%
94.522 1
2.0%
89.4244 1
2.0%
83.909 1
2.0%
69.8499 1
2.0%
67.2138 1
2.0%

RN
Real number (ℝ)

HIGH CORRELATION  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
2023-09-25T17:57:39.943493image/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
2023-09-25T17:57:40.286085image/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%

Interactions

2023-09-25T17:57:33.535591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:28.903772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:29.884932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:30.857531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:32.407320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:33.796308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:29.098728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:30.068242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:31.047372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:32.641519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:34.230948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:29.288222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:30.291826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:31.273515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:32.815305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:34.515838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:29.459595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:30.461171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:31.533583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:33.056475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:34.796046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:29.724421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:30.706556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:31.737506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:57:33.380494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-25T17:57:40.520312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
SHIP_OWNER_NM1.0001.0001.0001.0001.0001.0001.0001.000
SHIP_CNT1.0001.0000.0000.0000.0000.9350.9260.495
DPTR_HMS1.0000.0001.0000.7730.0000.0000.0000.000
ARVL_HMS1.0000.0000.7731.0000.0000.0000.0000.144
PRFMC1.0000.0000.0000.0001.0000.0000.0000.901
FUEL_CNSMP_QTY1.0000.9350.0000.0000.0001.0000.9500.304
NVGTN_DIST1.0000.9260.0000.0000.0000.9501.0000.399
RN1.0000.4950.0000.1440.9010.3040.3991.000
2023-09-25T17:57:40.885261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTR_HMSARVL_HMS
DPTR_HMS1.0000.600
ARVL_HMS0.6001.000
2023-09-25T17:57:41.275648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTPRFMCFUEL_CNSMP_QTYNVGTN_DISTRNDPTR_HMSARVL_HMS
SHIP_CNT1.0000.3270.9640.9540.3270.0000.000
PRFMC0.3271.0000.3180.2321.0000.0000.000
FUEL_CNSMP_QTY0.9640.3181.0000.9930.3180.0000.000
NVGTN_DIST0.9540.2320.9931.0000.2320.0000.000
RN0.3271.0000.3180.2321.0000.0000.000
DPTR_HMS0.0000.0000.0000.0000.0001.0000.600
ARVL_HMS0.0000.0000.0000.0000.0000.6001.000

Missing values

2023-09-25T17:57:34.996680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-25T17:57:35.249345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
0MdfnvrrqVklsslqj201-Jan-2023 12:00:0030-Apr-2023 18:00:0062755500217499000034.65812
1QruvhVklskroglqj401-Jan-2023 12:00:0030-Apr-2023 18:00:0071655000477802000066.6813
2OdqwdxVklsslqjFr101-Jan-2023 12:00:0030-Apr-2023 18:00:0073651900135001000018.32964
3FrvprvklsPdqdjhphqw201-Jan-2023 12:00:0030-Apr-2023 18:00:0073847500289517000039.20475
4WudqvzruogJurxs101-Apr-2023 12:00:0030-Apr-2023 18:00:00771197003757220004.871936
5SdflilfVklsPjpw101-Jan-2023 12:00:0030-Apr-2023 18:00:0080734500143252000017.74367
6WdqFdqjOrjlvwlfv101-Jan-2023 12:00:0030-Apr-2023 18:00:0081442800114235000014.02648
7Jhpdghsw201-Jan-2023 12:00:0030-Apr-2023 18:00:0082447700261247000031.68649
8IxnxvhlVdqjbr201-Jan-2023 12:00:0030-Apr-2023 18:00:0083980100241972000028.81310
9GrqjBrxqjVklsslqj301-Jan-2023 12:00:0030-Apr-2023 18:00:0084224700368904000043.811
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
39JrwrVklsslqj201-Jan-2023 12:00:0030-Apr-2023 18:00:0096561000344351000035.661541
40KVVfkliidkuwv501-Jan-2023 12:00:0030-Apr-2023 18:00:0097303300510772000052.492742
41RNHHPdulwlphJpeK501-Jan-2023 12:00:0030-Apr-2023 18:00:0097332300645435000066.312543
42VwduRfhdqPdulqh901-Jan-2023 12:00:0030-Apr-2023 18:00:009768290010870200000111.2844
43PdrplqjKxdaldqj101-Jan-2023 12:00:0030-Apr-2023 18:00:0097774700147633000015.099345
44VkdqjkdlMlqmldqj801-Jan-2023 12:00:0030-Apr-2023 18:00:009781460012460900000127.39346
45FrqpduVklsslqj201-Jan-2023 18:00:0030-Apr-2023 18:00:0098553600258309000026.2147
46SdqFrqwlqhqwdoVksj701-Jan-2023 12:00:0030-Apr-2023 18:00:0098634100932310000094.52248
47SWShodbdudqFdudnd301-Jan-2023 12:00:0030-Apr-2023 18:00:0098708000313186000031.728549
48OhswdVklsslqj401-Jan-2023 12:00:0030-Apr-2023 18:00:0098804200690146000069.849950