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_001329

Alerts

SHIP_CNT is highly overall correlated with PRFMC and 3 other fieldsHigh correlation
PRFMC is highly overall correlated with SHIP_CNT and 3 other fieldsHigh correlation
FUEL_CNSMP_QTY is highly overall correlated with SHIP_CNT and 3 other fieldsHigh correlation
NVGTN_DIST is highly overall correlated with SHIP_CNT and 4 other fieldsHigh correlation
RN is highly overall correlated with SHIP_CNT and 3 other fieldsHigh correlation
DPTR_HMS is highly overall correlated with NVGTN_DISTHigh correlation
DPTR_HMS is highly imbalanced (61.5%)Imbalance
ARVL_HMS is highly imbalanced (68.6%)Imbalance
SHPYRD_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 08:54:43.875014
Analysis finished2023-09-25 08:54:49.863515
Duration5.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHPYRD_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-09-25T17:54:50.222665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length13.102041
Min length5

Characters and Unicode

Total characters642
Distinct characters47
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 rowWrzdVEFr
2nd rowCkrxvkdqCkdredr
3rd rowQdqmlqjGrqjch
4th rowKxljdqjVbdug
5th rowCkhmldqjKrqjalq
ValueCountFrequency (%)
wrzdvefr 1
 
2.0%
fholnwhnqh 1
 
2.0%
nbrnxbrvklsbdug 1
 
2.0%
dwodqwlvvksbg 1
 
2.0%
shwhuvve 1
 
2.0%
pdzhlvepdzhl 1
 
2.0%
ghqwdvvklsbdug 1
 
2.0%
ckhmldqjkdqjfkdqj 1
 
2.0%
vwpdulqhehqrlug 1
 
2.0%
qdydojlmrq 1
 
2.0%
Other values (39) 39
79.6%
2023-09-25T17:54:51.036146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 71
 
11.1%
q 65
 
10.1%
l 56
 
8.7%
h 41
 
6.4%
r 38
 
5.9%
k 37
 
5.8%
V 31
 
4.8%
j 31
 
4.8%
x 20
 
3.1%
v 20
 
3.1%
Other values (37) 232
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 518
80.7%
Uppercase Letter 124
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 71
13.7%
q 65
12.5%
l 56
10.8%
h 41
 
7.9%
r 38
 
7.3%
k 37
 
7.1%
j 31
 
6.0%
x 20
 
3.9%
v 20
 
3.9%
g 19
 
3.7%
Other values (15) 120
23.2%
Uppercase Letter
ValueCountFrequency (%)
V 31
25.0%
K 12
 
9.7%
C 9
 
7.3%
E 9
 
7.3%
G 8
 
6.5%
M 7
 
5.6%
P 6
 
4.8%
W 6
 
4.8%
F 6
 
4.8%
B 5
 
4.0%
Other values (12) 25
20.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 642
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 71
 
11.1%
q 65
 
10.1%
l 56
 
8.7%
h 41
 
6.4%
r 38
 
5.9%
k 37
 
5.8%
V 31
 
4.8%
j 31
 
4.8%
x 20
 
3.1%
v 20
 
3.1%
Other values (37) 232
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 71
 
11.1%
q 65
 
10.1%
l 56
 
8.7%
h 41
 
6.4%
r 38
 
5.9%
k 37
 
5.8%
V 31
 
4.8%
j 31
 
4.8%
x 20
 
3.1%
v 20
 
3.1%
Other values (37) 232
36.1%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0816327
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:54:51.861331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310
95-th percentile33.2
Maximum75
Range74
Interquartile range (IQR)9

Descriptive statistics

Standard deviation15.290025
Coefficient of variation (CV)1.6836207
Kurtosis10.875852
Mean9.0816327
Median Absolute Deviation (MAD)2
Skewness3.1716562
Sum445
Variance233.78486
MonotonicityNot monotonic
2023-09-25T17:54:52.105122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 15
30.6%
2 8
16.3%
3 4
 
8.2%
4 3
 
6.1%
7 2
 
4.1%
18 2
 
4.1%
75 1
 
2.0%
20 1
 
2.0%
22 1
 
2.0%
6 1
 
2.0%
Other values (11) 11
22.4%
ValueCountFrequency (%)
1 15
30.6%
2 8
16.3%
3 4
 
8.2%
4 3
 
6.1%
5 1
 
2.0%
6 1
 
2.0%
7 2
 
4.1%
8 1
 
2.0%
9 1
 
2.0%
10 1
 
2.0%
ValueCountFrequency (%)
75 1
2.0%
69 1
2.0%
34 1
2.0%
32 1
2.0%
22 1
2.0%
20 1
2.0%
18 2
4.1%
14 1
2.0%
13 1
2.0%
12 1
2.0%

DPTR_HMS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2023 12:00:00
41 
01-Jan-2023 18:00:00
 
3
02-Jan-2023 00:00:00
 
2
03-Jan-2023 12:00:00
 
1
01-Jan-2023 06:00:00
 
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-Jan-2023 12:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2023 12:00:00 41
83.7%
01-Jan-2023 18:00:00 3
 
6.1%
02-Jan-2023 00:00:00 2
 
4.1%
03-Jan-2023 12:00:00 1
 
2.0%
01-Jan-2023 06:00:00 1
 
2.0%
01-Apr-2023 12:00:00 1
 
2.0%

Length

2023-09-25T17:54:52.335445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-25T17:54:52.531842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
01-jan-2023 45
45.9%
12:00:00 43
43.9%
18:00:00 3
 
3.1%
02-jan-2023 2
 
2.0%
00:00:00 2
 
2.0%
03-jan-2023 1
 
1.0%
06:00:00 1
 
1.0%
01-apr-2023 1
 
1.0%

ARVL_HMS
Categorical

IMBALANCE 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
30-Apr-2023 18:00:00
43 
31-Jan-2023 18:00:00
 
2
14-Jan-2023 12:00:00
 
1
29-Apr-2023 18:00:00
 
1
30-Apr-2023 06:00:00
 
1

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique4 ?
Unique (%)8.2%

Sample

1st row14-Jan-2023 12:00:00
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 43
87.8%
31-Jan-2023 18:00:00 2
 
4.1%
14-Jan-2023 12:00:00 1
 
2.0%
29-Apr-2023 18:00:00 1
 
2.0%
30-Apr-2023 06:00:00 1
 
2.0%
30-Apr-2023 00:00:00 1
 
2.0%

Length

2023-09-25T17:54:52.732144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-25T17:54:52.901629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
18:00:00 46
46.9%
30-apr-2023 45
45.9%
31-jan-2023 2
 
2.0%
14-jan-2023 1
 
1.0%
12:00:00 1
 
1.0%
29-apr-2023 1
 
1.0%
06:00:00 1
 
1.0%
00:00:00 1
 
1.0%

PRFMC
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73756992
Minimum42684000
Maximum95241700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:54:53.156160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42684000
5-th percentile51757720
Q163299200
median73284600
Q387581300
95-th percentile93524520
Maximum95241700
Range52557700
Interquartile range (IQR)24282100

Descriptive statistics

Standard deviation14822453
Coefficient of variation (CV)0.20096336
Kurtosis-1.0817794
Mean73756992
Median Absolute Deviation (MAD)13903700
Skewness-0.29273347
Sum3.6140926 × 109
Variance2.1970511 × 1014
MonotonicityStrictly increasing
2023-09-25T17:54:53.428459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
42684000 1
 
2.0%
87657900 1
 
2.0%
78578500 1
 
2.0%
79198900 1
 
2.0%
80827300 1
 
2.0%
81622400 1
 
2.0%
82200900 1
 
2.0%
82695600 1
 
2.0%
83997000 1
 
2.0%
85798500 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
42684000 1
2.0%
47015800 1
2.0%
51118600 1
2.0%
52716400 1
2.0%
52808600 1
2.0%
53322200 1
2.0%
53659300 1
2.0%
53943900 1
2.0%
55736100 1
2.0%
56629800 1
2.0%
ValueCountFrequency (%)
95241700 1
2.0%
94743800 1
2.0%
94494800 1
2.0%
92069100 1
2.0%
92047300 1
2.0%
91973000 1
2.0%
91499400 1
2.0%
90091300 1
2.0%
90019100 1
2.0%
88936600 1
2.0%

FUEL_CNSMP_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3176001 × 109
Minimum86732700
Maximum9.12618 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:54:53.683378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86732700
5-th percentile3.144832 × 108
Q18.19344 × 108
median2.97853 × 109
Q31.20455 × 1010
95-th percentile3.300306 × 1010
Maximum9.12618 × 1010
Range9.1175067 × 1010
Interquartile range (IQR)1.1226156 × 1010

Descriptive statistics

Standard deviation1.7513542 × 1010
Coefficient of variation (CV)1.8796194
Kurtosis12.965243
Mean9.3176001 × 109
Median Absolute Deviation (MAD)2.26314 × 109
Skewness3.428126
Sum4.5656241 × 1011
Variance3.0672416 × 1020
MonotonicityNot monotonic
2023-09-25T17:54:53.915349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
86732700 1
 
2.0%
12045500000 1
 
2.0%
36214700000 1
 
2.0%
286163000 1
 
2.0%
1266650000 1
 
2.0%
28185600000 1
 
2.0%
615950000 1
 
2.0%
866278000 1
 
2.0%
3899360000 1
 
2.0%
3787360000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
86732700 1
2.0%
286163000 1
2.0%
310984000 1
2.0%
319732000 1
2.0%
393343000 1
2.0%
469432000 1
2.0%
558952000 1
2.0%
567479000 1
2.0%
569486000 1
2.0%
615950000 1
2.0%
ValueCountFrequency (%)
91261800000 1
2.0%
74865500000 1
2.0%
36214700000 1
2.0%
28185600000 1
2.0%
23581000000 1
2.0%
20756100000 1
2.0%
17413800000 1
2.0%
17053800000 1
2.0%
16838500000 1
2.0%
14937400000 1
2.0%

NVGTN_DIST
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.62554
Minimum2.03197
Maximum1012.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:54:54.223031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.03197
5-th percentile4.426332
Q111.1803
median40.1053
Q3137.415
95-th percentile414.6502
Maximum1012.99
Range1010.958
Interquartile range (IQR)126.2347

Descriptive statistics

Standard deviation195.4046
Coefficient of variation (CV)1.7663606
Kurtosis11.839406
Mean110.62554
Median Absolute Deviation (MAD)29.6298
Skewness3.2677357
Sum5420.6514
Variance38182.958
MonotonicityNot monotonic
2023-09-25T17:54:54.467448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2.03197 1
 
2.0%
137.415 1
 
2.0%
460.873 1
 
2.0%
3.61322 1
 
2.0%
15.671 1
 
2.0%
345.316 1
 
2.0%
7.49323 1
 
2.0%
10.4755 1
 
2.0%
46.4226 1
 
2.0%
44.1425 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2.03197 1
2.0%
3.56681 1
2.0%
3.61322 1
2.0%
5.646 1
2.0%
7.05724 1
2.0%
7.49323 1
2.0%
8.62217 1
2.0%
8.88931 1
2.0%
10.4755 1
2.0%
10.557 1
2.0%
ValueCountFrequency (%)
1012.99 1
2.0%
813.337 1
2.0%
460.873 1
2.0%
345.316 1
2.0%
253.491 1
2.0%
247.591 1
2.0%
239.878 1
2.0%
219.076 1
2.0%
189.447 1
2.0%
163.252 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
2023-09-25T17:54:54.737236image/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
2023-09-25T17:54:55.022764image/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

2023-09-25T17:54:48.389716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:44.735498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:45.574014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:46.488391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:47.384050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:48.596930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:44.903573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:45.734756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:46.655746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:47.628284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:48.739666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:45.054220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:45.887923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:46.819884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:47.914125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:48.892403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:45.217880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:46.182388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:46.963277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:48.044508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:49.053466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:45.412164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:46.352772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:47.126734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-25T17:54:48.200878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-25T17:54:55.213125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
SHPYRD_NM1.0001.0001.0001.0001.0001.0001.0001.000
SHIP_CNT1.0001.0000.5290.0000.2240.9120.9040.607
DPTR_HMS1.0000.5291.0000.7230.0000.7760.7250.000
ARVL_HMS1.0000.0000.7231.0000.0000.0000.0000.000
PRFMC1.0000.2240.0000.0001.0000.0000.0000.984
FUEL_CNSMP_QTY1.0000.9120.7760.0000.0001.0000.9600.558
NVGTN_DIST1.0000.9040.7250.0000.0000.9601.0000.560
RN1.0000.6070.0000.0000.9840.5580.5601.000
2023-09-25T17:54:55.402971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTR_HMSARVL_HMS
DPTR_HMS1.0000.334
ARVL_HMS0.3341.000
2023-09-25T17:54:55.562789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTPRFMCFUEL_CNSMP_QTYNVGTN_DISTRNDPTR_HMSARVL_HMS
SHIP_CNT1.0000.5920.9510.9500.5920.3860.000
PRFMC0.5921.0000.5950.5081.0000.0000.199
FUEL_CNSMP_QTY0.9510.5951.0000.9880.5950.3840.000
NVGTN_DIST0.9500.5080.9881.0000.5080.5320.000
RN0.5921.0000.5950.5081.0000.0000.000
DPTR_HMS0.3860.0000.3840.5320.0001.0000.334
ARVL_HMS0.0000.1990.0000.0000.0000.3341.000

Missing values

2023-09-25T17:54:49.499022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-25T17:54:49.756245image/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

SHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
0WrzdVEFr101-Jan-2023 12:00:0014-Jan-2023 12:00:0042684000867327002.031972
1CkrxvkdqCkdredr101-Jan-2023 12:00:0030-Apr-2023 18:00:004701580055895200011.88863
2QdqmlqjGrqjch201-Jan-2023 12:00:0030-Apr-2023 18:00:0051118600117986000023.08084
3KxljdqjVbdug201-Jan-2023 12:00:0030-Apr-2023 18:00:0052716400126450000023.98685
4CkhmldqjKrqjalq101-Jan-2023 12:00:0030-Apr-2023 18:00:00528086004694320008.889316
5UhprqwrzdVE101-Jan-2023 12:00:0030-Apr-2023 18:00:0053322200104021000019.5087
6QdpWulhxVE101-Jan-2023 12:00:0030-Apr-2023 18:00:005365930056747900010.57568
7VklqdVE101-Jan-2023 12:00:0030-Apr-2023 18:00:005394390056948600010.5579
8CkrqjerRiivkruh101-Jan-2023 12:00:0030-Apr-2023 18:00:00557361003933430007.0572410
9MldqjvxBlckhqj102-Jan-2023 00:00:0030-Apr-2023 18:00:00566298003197320005.64611
SHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
39VkdqgrqjZhlkdlVB1201-Jan-2023 12:00:0030-Apr-2023 00:00:008893660013552100000152.3841
40VkxqwldqVkxqjdr1801-Jan-2023 12:00:0030-Apr-2023 18:00:009001910017053800000189.44742
41GdhVxqVklsexloglqj7501-Jan-2023 12:00:0030-Apr-2023 18:00:0090091300912618000001012.9943
42VklqNrfklMbxnr1301-Jan-2023 12:00:0030-Apr-2023 18:00:009149940014937400000163.25244
43QdqmlqjBlfkxq1101-Jan-2023 12:00:0030-Apr-2023 18:00:009197300014281600000155.28145
44MMVlhwdv6902-Jan-2023 00:00:0030-Apr-2023 18:00:009204730074865500000813.33746
45PKLVklprqrvhnl601-Jan-2023 12:00:0031-Jan-2023 18:00:0092069100516500000056.099247
46PxudndplKlgh301-Jan-2023 18:00:0030-Apr-2023 18:00:0094494800411332000043.529648
47FVFTlqjvkdqVB2201-Jan-2023 12:00:0030-Apr-2023 18:00:009474380020756100000219.07649
48KdndwdCrvhq2001-Jan-2023 12:00:0030-Apr-2023 18:00:009524170023581000000247.59150