Overview

Dataset statistics

Number of variables24
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory212.7 B

Variable types

Numeric17
Text1
Categorical6

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001319

Alerts

RL_POWER has constant value ""Constant
SHIP_KIND is highly imbalanced (85.6%)Imbalance
DPTR_HMS is highly imbalanced (69.6%)Imbalance
ARVL_HMS is highly imbalanced (85.6%)Imbalance
MMSI has unique valuesUnique
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM has unique valuesUnique
DPTRP_LA has unique valuesUnique
DPTRP_LO has unique valuesUnique
DTNT_LA has unique valuesUnique
DTNT_LO has unique valuesUnique
ADDTI_RSTC has unique valuesUnique
TOT_RSTC has unique valuesUnique
NOX has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:32:00.271914
Analysis finished2023-12-10 14:32:00.497077
Duration0.23 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%
Mean5.7134297 × 108
Minimum5.4230533 × 108
Maximum8.9631793 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:00.572723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.4230533 × 108
5-th percentile5.4231573 × 108
Q15.4238633 × 108
median5.4244533 × 108
Q35.4255233 × 108
95-th percentile8.9631739 × 108
Maximum8.9631793 × 108
Range3.540126 × 108
Interquartile range (IQR)166000

Descriptive statistics

Standard deviation97892819
Coefficient of variation (CV)0.1713381
Kurtosis8.2799669
Mean5.7134297 × 108
Median Absolute Deviation (MAD)84000
Skewness3.1533174
Sum2.7995805 × 1010
Variance9.583004 × 1015
MonotonicityNot monotonic
2023-12-10T23:32:00.703734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
896317933 1
 
2.0%
542548333 1
 
2.0%
542463333 1
 
2.0%
542466333 1
 
2.0%
542469333 1
 
2.0%
542488333 1
 
2.0%
542403333 1
 
2.0%
542429333 1
 
2.0%
542421333 1
 
2.0%
542422333 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
542305333 1
2.0%
542308333 1
2.0%
542313333 1
2.0%
542319333 1
2.0%
542328333 1
2.0%
542329333 1
2.0%
542361333 1
2.0%
542362333 1
2.0%
542366333 1
2.0%
542367333 1
2.0%
ValueCountFrequency (%)
896317933 1
2.0%
896317833 1
2.0%
896317633 1
2.0%
896317033 1
2.0%
542697333 1
2.0%
542657333 1
2.0%
542637333 1
2.0%
542582333 1
2.0%
542581333 1
2.0%
542564333 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2570424.8
Minimum2001866
Maximum2955586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:00.815477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001866
5-th percentile2008033.6
Q12421810
median2685351
Q32781363
95-th percentile2955557.2
Maximum2955586
Range953720
Interquartile range (IQR)359553

Descriptive statistics

Standard deviation339526.34
Coefficient of variation (CV)0.13208958
Kurtosis-0.86255515
Mean2570424.8
Median Absolute Deviation (MAD)138115
Skewness-0.79996777
Sum1.2595081 × 108
Variance1.1527813 × 1011
MonotonicityNot monotonic
2023-12-10T23:32:00.927845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2547236 1
 
2.0%
2646264 1
 
2.0%
2780333 1
 
2.0%
2008008 1
 
2.0%
2001866 1
 
2.0%
2781313 1
 
2.0%
2001878 1
 
2.0%
2646240 1
 
2.0%
2781301 1
 
2.0%
2781363 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2001866 1
2.0%
2001878 1
2.0%
2008008 1
2.0%
2008072 1
2.0%
2008084 1
2.0%
2008096 1
2.0%
2013704 1
2.0%
2013716 1
2.0%
2013778 1
2.0%
2013780 1
2.0%
ValueCountFrequency (%)
2955586 1
2.0%
2955574 1
2.0%
2955562 1
2.0%
2955550 1
2.0%
2955548 1
2.0%
2955536 1
2.0%
2941494 1
2.0%
2922450 1
2.0%
2789224 1
2.0%
2789092 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:32:01.144160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length12.469388
Min length8

Characters and Unicode

Total characters611
Distinct characters41
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 rowFdurolqhPdhuvn
2nd rowDSProohu
3rd rowFduvwhqPdhuvn
4th rowFruqholxvPdhuvn
5th rowPdhuvnHglqexujk
ValueCountFrequency (%)
fdurolqhpdhuvn 1
 
2.0%
vhdjrsludhxv 1
 
2.0%
ukrqhpdhuvn 1
 
2.0%
yxrnvlpdhuvn 1
 
2.0%
yloqldpdhuvn 1
 
2.0%
pdhuvnhyrud 1
 
2.0%
ydjdpdhuvn 1
 
2.0%
pdhuvnedowlpruh 1
 
2.0%
pdhuvnhoed 1
 
2.0%
pdhuvnhgprqwrq 1
 
2.0%
Other values (39) 39
79.6%
2023-12-10T23:32:01.465099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 96
15.7%
h 56
 
9.2%
u 54
 
8.8%
v 48
 
7.9%
P 39
 
6.4%
q 38
 
6.2%
n 36
 
5.9%
r 29
 
4.7%
w 21
 
3.4%
l 20
 
3.3%
Other values (31) 174
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 493
80.7%
Uppercase Letter 118
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 96
19.5%
h 56
11.4%
u 54
11.0%
v 48
9.7%
q 38
 
7.7%
n 36
 
7.3%
r 29
 
5.9%
w 21
 
4.3%
l 20
 
4.1%
o 20
 
4.1%
Other values (14) 75
15.2%
Uppercase Letter
ValueCountFrequency (%)
P 39
33.1%
V 17
14.4%
F 15
 
12.7%
H 8
 
6.8%
D 8
 
6.8%
Y 6
 
5.1%
U 6
 
5.1%
Q 5
 
4.2%
E 4
 
3.4%
S 2
 
1.7%
Other values (7) 8
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 611
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 96
15.7%
h 56
 
9.2%
u 54
 
8.8%
v 48
 
7.9%
P 39
 
6.4%
q 38
 
6.2%
n 36
 
5.9%
r 29
 
4.7%
w 21
 
3.4%
l 20
 
3.3%
Other values (31) 174
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 96
15.7%
h 56
 
9.2%
u 54
 
8.8%
v 48
 
7.9%
P 39
 
6.4%
q 38
 
6.2%
n 36
 
5.9%
r 29
 
4.7%
w 21
 
3.4%
l 20
 
3.3%
Other values (31) 174
28.5%

SHIP_KIND
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Container
48 
Cargo or Containership
 
1

Length

Max length22
Median length9
Mean length9.2653061
Min length9

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowContainer
2nd rowContainer
3rd rowContainer
4th rowContainer
5th rowContainer

Common Values

ValueCountFrequency (%)
Container 48
98.0%
Cargo or Containership 1
 
2.0%

Length

2023-12-10T23:32:01.575790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:01.654183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
container 48
94.1%
cargo 1
 
2.0%
or 1
 
2.0%
containership 1
 
2.0%

SHIP_WDTH
Real number (ℝ)

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.47551
Minimum15.6
Maximum58.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:01.727224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.6
5-th percentile30.28
Q135.2
median42.8
Q348.2
95-th percentile58.6
Maximum58.6
Range43
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.9674999
Coefficient of variation (CV)0.21112165
Kurtosis0.51521
Mean42.47551
Median Absolute Deviation (MAD)5.4
Skewness-0.27167673
Sum2081.3
Variance80.416054
MonotonicityNot monotonic
2023-12-10T23:32:01.814032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
48.2 13
26.5%
42.8 9
18.4%
35.2 6
12.2%
58.6 5
 
10.2%
32.3 5
 
10.2%
42.0 3
 
6.1%
37.4 2
 
4.1%
29.8 2
 
4.1%
48.4 2
 
4.1%
31.0 1
 
2.0%
ValueCountFrequency (%)
15.6 1
 
2.0%
29.8 2
 
4.1%
31.0 1
 
2.0%
32.3 5
 
10.2%
35.2 6
12.2%
37.4 2
 
4.1%
42.0 3
 
6.1%
42.8 9
18.4%
48.2 13
26.5%
48.4 2
 
4.1%
ValueCountFrequency (%)
58.6 5
 
10.2%
48.4 2
 
4.1%
48.2 13
26.5%
42.8 9
18.4%
42.0 3
 
6.1%
37.4 2
 
4.1%
35.2 6
12.2%
32.3 5
 
10.2%
31.0 1
 
2.0%
29.8 2
 
4.1%

SHIP_LNTH
Real number (ℝ)

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.89592
Minimum74.2
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:01.901365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74.2
5-th percentile196
Q1278.2
median313.5
Q3350
95-th percentile378
Maximum378
Range303.8
Interquartile range (IQR)71.8

Descriptive statistics

Standard deviation67.362211
Coefficient of variation (CV)0.23077476
Kurtosis0.79968611
Mean291.89592
Median Absolute Deviation (MAD)36.5
Skewness-0.99236344
Sum14302.9
Variance4537.6675
MonotonicityNot monotonic
2023-12-10T23:32:01.987697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
350.0 8
16.3%
196.0 6
12.2%
313.5 6
12.2%
331.5 5
10.2%
378.0 5
10.2%
278.2 5
10.2%
286.8 3
 
6.1%
319.0 3
 
6.1%
235.0 2
 
4.1%
198.7 2
 
4.1%
Other values (4) 4
8.2%
ValueCountFrequency (%)
74.2 1
 
2.0%
176.2 1
 
2.0%
196.0 6
12.2%
198.7 2
 
4.1%
235.0 2
 
4.1%
278.2 5
10.2%
285.2 1
 
2.0%
286.8 3
6.1%
287.0 1
 
2.0%
313.5 6
12.2%
ValueCountFrequency (%)
378.0 5
10.2%
350.0 8
16.3%
331.5 5
10.2%
319.0 3
 
6.1%
313.5 6
12.2%
287.0 1
 
2.0%
286.8 3
 
6.1%
285.2 1
 
2.0%
278.2 5
10.2%
235.0 2
 
4.1%

SHIP_HGHT
Real number (ℝ)

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.35102
Minimum6.2
Maximum33.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:02.077160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile16.8
Q121.4
median24.5
Q329.8
95-th percentile33.2
Maximum33.2
Range27
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation5.6007114
Coefficient of variation (CV)0.22999904
Kurtosis0.83483259
Mean24.35102
Median Absolute Deviation (MAD)5.2
Skewness-0.52808689
Sum1193.2
Variance31.367968
MonotonicityNot monotonic
2023-12-10T23:32:02.448727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
29.9 6
12.2%
18.1 6
12.2%
26.8 6
12.2%
24.1 5
10.2%
33.2 5
10.2%
21.4 5
10.2%
24.5 3
6.1%
24.6 3
6.1%
19.3 2
 
4.1%
16.4 2
 
4.1%
Other values (5) 6
12.2%
ValueCountFrequency (%)
6.2 1
 
2.0%
16.4 2
 
4.1%
17.4 1
 
2.0%
18.1 6
12.2%
19.3 2
 
4.1%
21.4 5
10.2%
24.1 5
10.2%
24.2 1
 
2.0%
24.5 3
6.1%
24.6 3
6.1%
ValueCountFrequency (%)
33.2 5
10.2%
29.9 6
12.2%
29.8 2
 
4.1%
26.8 6
12.2%
24.8 1
 
2.0%
24.6 3
6.1%
24.5 3
6.1%
24.2 1
 
2.0%
24.1 5
10.2%
21.4 5
10.2%

SHIP_OWNER_NM
Categorical

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
Pdhuvn
21 
KdpexujVxg
10 
3
5 
PlqvkhqjIlqdqfldo
3 
QdyljduhFdslwdo
3 
Other values (4)
7 

Length

Max length17
Median length15
Mean length8.2857143
Min length1

Unique

Unique2 ?
Unique (%)4.1%

Sample

1st rowPdhuvn
2nd rowPdhuvn
3rd rowPdhuvn
4th rowPdhuvn
5th rowPlqvkhqjIlqdqfldo

Common Values

ValueCountFrequency (%)
Pdhuvn 21
42.9%
KdpexujVxg 10
20.4%
3 5
 
10.2%
PlqvkhqjIlqdqfldo 3
 
6.1%
QdyljduhFdslwdo 3
 
6.1%
ErFrpOhdvlqj 3
 
6.1%
PdhuvnFrpsdqb 2
 
4.1%
UrbdoDufwlfOlqh 1
 
2.0%
QD 1
 
2.0%

Length

2023-12-10T23:32:02.553991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:02.653934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pdhuvn 21
42.9%
kdpexujvxg 10
20.4%
3 5
 
10.2%
plqvkhqjilqdqfldo 3
 
6.1%
qdyljduhfdslwdo 3
 
6.1%
erfrpohdvlqj 3
 
6.1%
pdhuvnfrpsdqb 2
 
4.1%
urbdodufwlfolqh 1
 
2.0%
qd 1
 
2.0%

DRAFT
Real number (ℝ)

Distinct10
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.967347
Minimum4.5
Maximum16.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:02.750483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile11.52
Q112.5
median14.5
Q316
95-th percentile16.5
Maximum16.5
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.1244007
Coefficient of variation (CV)0.15209765
Kurtosis6.8040835
Mean13.967347
Median Absolute Deviation (MAD)1.5
Skewness-1.8297337
Sum684.4
Variance4.5130782
MonotonicityNot monotonic
2023-12-10T23:32:02.874204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
14.5 9
18.4%
16.0 8
16.3%
12.0 7
14.3%
14.0 6
12.2%
16.5 5
10.2%
12.5 5
10.2%
15.0 4
8.2%
13.0 2
 
4.1%
11.2 2
 
4.1%
4.5 1
 
2.0%
ValueCountFrequency (%)
4.5 1
 
2.0%
11.2 2
 
4.1%
12.0 7
14.3%
12.5 5
10.2%
13.0 2
 
4.1%
14.0 6
12.2%
14.5 9
18.4%
15.0 4
8.2%
16.0 8
16.3%
16.5 5
10.2%
ValueCountFrequency (%)
16.5 5
10.2%
16.0 8
16.3%
15.0 4
8.2%
14.5 9
18.4%
14.0 6
12.2%
13.0 2
 
4.1%
12.5 5
10.2%
12.0 7
14.3%
11.2 2
 
4.1%
4.5 1
 
2.0%

SHPYRD_NM
Categorical

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
KbxqgdlKLXovdq
15 
GdhzrrGVPH
9 
Yronvzhuiw
7 
FRVFRKLCkrxvkdq
6 
RghqvhOlqgr
5 
Other values (4)
7 

Length

Max length15
Median length14
Mean length12.081633
Min length2

Unique

Unique2 ?
Unique (%)4.1%

Sample

1st rowRghqvhOlqgr
2nd rowRghqvhOlqgr
3rd rowRghqvhOlqgr
4th rowRghqvhOlqgr
5th rowKbxqgdlKLXovdq

Common Values

ValueCountFrequency (%)
KbxqgdlKLXovdq 15
30.6%
GdhzrrGVPH 9
18.4%
Yronvzhuiw 7
14.3%
FRVFRKLCkrxvkdq 6
 
12.2%
RghqvhOlqgr 5
 
10.2%
KbxqgdlVdpkrKL 3
 
6.1%
VdpvxqjKL 2
 
4.1%
KxdqjsxZhqfkrqj 1
 
2.0%
QD 1
 
2.0%

Length

2023-12-10T23:32:02.987320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:03.092904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kbxqgdlklxovdq 15
30.6%
gdhzrrgvph 9
18.4%
yronvzhuiw 7
14.3%
frvfrklckrxvkdq 6
 
12.2%
rghqvholqgr 5
 
10.2%
kbxqgdlvdpkrkl 3
 
6.1%
vdpvxqjkl 2
 
4.1%
kxdqjsxzhqfkrqj 1
 
2.0%
qd 1
 
2.0%

BULD_YR
Real number (ℝ)

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.3469
Minimum1998
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:03.197551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile2000
Q12008
median2011
Q32018
95-th percentile2019
Maximum2020
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7646589
Coefficient of variation (CV)0.0028660689
Kurtosis-0.22095164
Mean2011.3469
Median Absolute Deviation (MAD)3
Skewness-0.45048726
Sum98556
Variance33.231293
MonotonicityNot monotonic
2023-12-10T23:32:03.301585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2018 8
16.3%
2011 7
14.3%
2010 5
10.2%
2008 5
10.2%
2000 4
8.2%
2013 4
8.2%
2019 3
 
6.1%
2014 3
 
6.1%
2006 3
 
6.1%
2007 2
 
4.1%
Other values (3) 5
10.2%
ValueCountFrequency (%)
1998 1
 
2.0%
2000 4
8.2%
2006 3
6.1%
2007 2
 
4.1%
2008 5
10.2%
2010 5
10.2%
2011 7
14.3%
2012 2
 
4.1%
2013 4
8.2%
2014 3
6.1%
ValueCountFrequency (%)
2020 2
 
4.1%
2019 3
 
6.1%
2018 8
16.3%
2014 3
 
6.1%
2013 4
8.2%
2012 2
 
4.1%
2011 7
14.3%
2010 5
10.2%
2008 5
10.2%
2007 2
 
4.1%

DDWGHT
Real number (ℝ)

Distinct18
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101524.08
Minimum2700
Maximum190326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:03.412941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2700
5-th percentile34126.4
Q153701
median109657
Q3146046
95-th percentile190326
Maximum190326
Range187626
Interquartile range (IQR)92345

Descriptive statistics

Standard deviation50594.28
Coefficient of variation (CV)0.49834757
Kurtosis-0.89672464
Mean101524.08
Median Absolute Deviation (MAD)39066
Skewness0.054484221
Sum4974680
Variance2.5597811 × 109
MonotonicityNot monotonic
2023-12-10T23:32:03.503716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
40000 6
12.2%
124458 6
12.2%
190326 5
10.2%
146046 5
10.2%
53701 5
10.2%
110381 4
8.2%
93551 3
 
6.1%
107978 3
 
6.1%
153514 2
 
4.1%
61983 2
 
4.1%
Other values (8) 8
16.3%
ValueCountFrequency (%)
2700 1
 
2.0%
25668 1
 
2.0%
33796 1
 
2.0%
34622 1
 
2.0%
40000 6
12.2%
53701 5
10.2%
61983 2
 
4.1%
93025 1
 
2.0%
93551 3
6.1%
107978 3
6.1%
ValueCountFrequency (%)
190326 5
10.2%
153514 2
 
4.1%
148723 1
 
2.0%
146046 5
10.2%
124458 6
12.2%
112271 1
 
2.0%
110381 4
8.2%
109657 1
 
2.0%
107978 3
6.1%
93551 3
6.1%

DPTR_HMS
Categorical

IMBALANCE 

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2023 12:00:00
43 
01-Mar-2023 12:00:00
 
1
27-Jan-2023 18:00:00
 
1
26-Jan-2023 12:00:00
 
1
01-Apr-2023 12:00:00
 
1
Other values (2)
 
2

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique6 ?
Unique (%)12.2%

Sample

1st row01-Mar-2023 12:00:00
2nd row27-Jan-2023 18:00:00
3rd row26-Jan-2023 12:00:00
4th row01-Apr-2023 12:00:00
5th row01-Jan-2023 12:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2023 12:00:00 43
87.8%
01-Mar-2023 12:00:00 1
 
2.0%
27-Jan-2023 18:00:00 1
 
2.0%
26-Jan-2023 12:00:00 1
 
2.0%
01-Apr-2023 12:00:00 1
 
2.0%
01-Jan-2023 18:00:00 1
 
2.0%
02-Jan-2023 12:00:00 1
 
2.0%

Length

2023-12-10T23:32:03.609865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:03.734255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12:00:00 47
48.0%
01-jan-2023 44
44.9%
18:00:00 2
 
2.0%
01-mar-2023 1
 
1.0%
27-jan-2023 1
 
1.0%
26-jan-2023 1
 
1.0%
01-apr-2023 1
 
1.0%
02-jan-2023 1
 
1.0%

ARVL_HMS
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
30-Apr-2023 18:00:00
48 
30-Apr-2023 12:00:00
 
1

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique1 ?
Unique (%)2.0%

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 48
98.0%
30-Apr-2023 12:00:00 1
 
2.0%

Length

2023-12-10T23:32:03.843717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:03.935159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30-apr-2023 49
50.0%
18:00:00 48
49.0%
12:00:00 1
 
1.0%

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.538092
Minimum-34.580399
Maximum64.167503
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)10.2%
Memory size573.0 B
2023-12-10T23:32:04.062172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-34.580399
5-th percentile-13.862469
Q124.7607
median34.181499
Q344.103802
95-th percentile57.264059
Maximum64.167503
Range98.747902
Interquartile range (IQR)19.343102

Descriptive statistics

Standard deviation22.569756
Coefficient of variation (CV)0.76408982
Kurtosis0.90219761
Mean29.538092
Median Absolute Deviation (MAD)9.922303
Skewness-1.004918
Sum1447.3665
Variance509.39387
MonotonicityNot monotonic
2023-12-10T23:32:04.208889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
27.3808 1
 
2.0%
-3.69116 1
 
2.0%
29.8451 1
 
2.0%
56.178699 1
 
2.0%
57.007801 1
 
2.0%
50.6786 1
 
2.0%
54.601002 1
 
2.0%
30.9231 1
 
2.0%
30.004101 1
 
2.0%
34.603298 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-34.580399 1
2.0%
-29.5609 1
2.0%
-18.139601 1
2.0%
-7.44677 1
2.0%
-3.69116 1
2.0%
3.05562 1
2.0%
4.01074 1
2.0%
4.56724 1
2.0%
5.71607 1
2.0%
5.71957 1
2.0%
ValueCountFrequency (%)
64.167503 1
2.0%
57.509998 1
2.0%
57.434898 1
2.0%
57.007801 1
2.0%
56.178699 1
2.0%
54.6679 1
2.0%
54.601002 1
2.0%
53.197701 1
2.0%
52.070702 1
2.0%
51.3391 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.254779
Minimum-173.398
Maximum176.035
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:32:04.328408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-173.398
5-th percentile-164.2916
Q1-26.467199
median11.2624
Q3121.335
95-th percentile148.389
Maximum176.035
Range349.433
Interquartile range (IQR)147.8022

Descriptive statistics

Standard deviation93.921742
Coefficient of variation (CV)4.8778406
Kurtosis-0.53783498
Mean19.254779
Median Absolute Deviation (MAD)67.7988
Skewness-0.31259286
Sum943.48416
Variance8821.2937
MonotonicityNot monotonic
2023-12-10T23:32:04.464687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-173.397995 1
 
2.0%
41.4622 1
 
2.0%
123.277 1
 
2.0%
11.2624 1
 
2.0%
24.0961 1
 
2.0%
176.035004 1
 
2.0%
14.5846 1
 
2.0%
130.436996 1
 
2.0%
-167.046997 1
 
2.0%
127.997002 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-173.397995 1
2.0%
-167.046997 1
2.0%
-166.692001 1
2.0%
-160.690994 1
2.0%
-123.306999 1
2.0%
-116.238998 1
2.0%
-86.737099 1
2.0%
-80.375504 1
2.0%
-58.3671 1
2.0%
-56.5364 1
2.0%
ValueCountFrequency (%)
176.035004 1
2.0%
162.990005 1
2.0%
160.356995 1
2.0%
130.436996 1
2.0%
128.807999 1
2.0%
128.804993 1
2.0%
128.408005 1
2.0%
127.997002 1
2.0%
123.57 1
2.0%
123.277 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.260305
Minimum-31.909599
Maximum61.812099
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)14.3%
Memory size573.0 B
2023-12-10T23:32:04.604878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-31.909599
5-th percentile-27.12102
Q117.4841
median35.669701
Q340.8423
95-th percentile56.13564
Maximum61.812099
Range93.721698
Interquartile range (IQR)23.3582

Descriptive statistics

Standard deviation25.978855
Coefficient of variation (CV)0.98928227
Kurtosis0.18037142
Mean26.260305
Median Absolute Deviation (MAD)14.092399
Skewness-1.0241096
Sum1286.755
Variance674.90088
MonotonicityNot monotonic
2023-12-10T23:32:04.720746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-27.592501 1
 
2.0%
7.0485 1
 
2.0%
38.959202 1
 
2.0%
54.928001 1
 
2.0%
56.542 1
 
2.0%
10.2621 1
 
2.0%
55.5261 1
 
2.0%
30.938999 1
 
2.0%
36.000401 1
 
2.0%
49.203201 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-31.909599 1
2.0%
-30.235399 1
2.0%
-27.592501 1
2.0%
-26.413799 1
2.0%
-25.047501 1
2.0%
-23.9657 1
2.0%
-22.92 1
2.0%
4.18372 1
2.0%
5.48845 1
2.0%
7.0485 1
2.0%
ValueCountFrequency (%)
61.812099 1
2.0%
59.329201 1
2.0%
56.542 1
2.0%
55.5261 1
2.0%
54.928001 1
2.0%
54.3801 1
2.0%
53.381699 1
2.0%
51.337601 1
2.0%
49.7621 1
2.0%
49.203201 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.843053
Minimum-139.455
Maximum173.284
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)32.7%
Memory size573.0 B
2023-12-10T23:32:04.822312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-139.455
5-th percentile-110.3324
Q1-9.34591
median15.2042
Q3107.054
95-th percentile137.8396
Maximum173.284
Range312.739
Interquartile range (IQR)116.39991

Descriptive statistics

Standard deviation79.551405
Coefficient of variation (CV)2.7580785
Kurtosis-0.65884565
Mean28.843053
Median Absolute Deviation (MAD)59.7257
Skewness-0.14091945
Sum1413.3096
Variance6328.4261
MonotonicityNot monotonic
2023-12-10T23:32:04.955500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-90.691902 1
 
2.0%
52.2995 1
 
2.0%
-124.658997 1
 
2.0%
17.974199 1
 
2.0%
11.7116 1
 
2.0%
107.054001 1
 
2.0%
15.1815 1
 
2.0%
129.897003 1
 
2.0%
120.196999 1
 
2.0%
173.283997 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-139.455002 1
2.0%
-124.658997 1
2.0%
-112.832001 1
2.0%
-106.583 1
2.0%
-90.691902 1
2.0%
-71.737396 1
2.0%
-49.721001 1
2.0%
-47.216499 1
2.0%
-46.291698 1
2.0%
-44.5215 1
2.0%
ValueCountFrequency (%)
173.283997 1
2.0%
164.973007 1
2.0%
139.798004 1
2.0%
134.901993 1
2.0%
129.897003 1
2.0%
122.821999 1
2.0%
122.160004 1
2.0%
121.310997 1
2.0%
120.984001 1
2.0%
120.203003 1
2.0%

ADDTI_RSTC
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40936.361
Minimum3020.66
Maximum97067.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:05.114282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3020.66
5-th percentile12048.1
Q121124.4
median39332.9
Q355026.6
95-th percentile88114.4
Maximum97067.7
Range94047.04
Interquartile range (IQR)33902.2

Descriptive statistics

Standard deviation23457.075
Coefficient of variation (CV)0.57301319
Kurtosis-0.19355567
Mean40936.361
Median Absolute Deviation (MAD)16626.1
Skewness0.61032751
Sum2005881.7
Variance5.5023436 × 108
MonotonicityNot monotonic
2023-12-10T23:32:05.273038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
31729.6 1
 
2.0%
13895.3 1
 
2.0%
89567.8 1
 
2.0%
14392.1 1
 
2.0%
19864.7 1
 
2.0%
90349.1 1
 
2.0%
16931.9 1
 
2.0%
21124.4 1
 
2.0%
30148.1 1
 
2.0%
62610.9 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3020.66 1
2.0%
9495.33 1
2.0%
11561.9 1
2.0%
12777.4 1
2.0%
12985.4 1
2.0%
13279.7 1
2.0%
13895.3 1
2.0%
14392.1 1
2.0%
16931.9 1
2.0%
18654.8 1
2.0%
ValueCountFrequency (%)
97067.7 1
2.0%
90349.1 1
2.0%
89567.8 1
2.0%
85934.3 1
2.0%
83702.2 1
2.0%
69550.7 1
2.0%
62610.9 1
2.0%
60478.8 1
2.0%
58871.4 1
2.0%
57547.2 1
2.0%

TOT_RSTC
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177537.42
Minimum6737.18
Maximum393892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:05.401430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6737.18
5-th percentile45447.76
Q199030
median155373
Q3253504
95-th percentile348407.4
Maximum393892
Range387154.82
Interquartile range (IQR)154474

Descriptive statistics

Standard deviation100025.86
Coefficient of variation (CV)0.56340722
Kurtosis-0.7393843
Mean177537.42
Median Absolute Deviation (MAD)73791
Skewness0.40453393
Sum8699333.6
Variance1.0005174 × 1010
MonotonicityNot monotonic
2023-12-10T23:32:05.530222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
204122.0 1
 
2.0%
99030.0 1
 
2.0%
305496.0 1
 
2.0%
53509.7 1
 
2.0%
68367.5 1
 
2.0%
323961.0 1
 
2.0%
55260.9 1
 
2.0%
76213.9 1
 
2.0%
70000.7 1
 
2.0%
195078.0 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
6737.18 1
2.0%
31171.6 1
2.0%
42591.6 1
2.0%
49732.0 1
2.0%
53509.7 1
2.0%
55260.9 1
2.0%
68367.5 1
2.0%
70000.7 1
2.0%
72899.8 1
2.0%
76213.9 1
2.0%
ValueCountFrequency (%)
393892.0 1
2.0%
375403.0 1
2.0%
360307.0 1
2.0%
330558.0 1
2.0%
323961.0 1
2.0%
321858.0 1
2.0%
312156.0 1
2.0%
305496.0 1
2.0%
288475.0 1
2.0%
282518.0 1
2.0%

RL_POWER
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

2023-12-10T23:32:05.650385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:05.742046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

FUEL_CNSMP_QTY
Real number (ℝ)

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7443534 × 109
Minimum4.16408 × 108
Maximum9.55935 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:05.873176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.16408 × 108
5-th percentile1.643636 × 109
Q12.69471 × 109
median4.50829 × 109
Q36.29703 × 109
95-th percentile8.904678 × 109
Maximum9.55935 × 109
Range9.142942 × 109
Interquartile range (IQR)3.60232 × 109

Descriptive statistics

Standard deviation2.3467799 × 109
Coefficient of variation (CV)0.49464694
Kurtosis-0.65327075
Mean4.7443534 × 109
Median Absolute Deviation (MAD)1.81358 × 109
Skewness0.39029079
Sum2.3247332 × 1011
Variance5.507376 × 1018
MonotonicityNot monotonic
2023-12-10T23:32:06.072541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3931810000 2
 
4.1%
5660230000 1
 
2.0%
2694710000 1
 
2.0%
1843190000 1
 
2.0%
2146920000 1
 
2.0%
8926830000 1
 
2.0%
2054090000 1
 
2.0%
2342410000 1
 
2.0%
2460350000 1
 
2.0%
5583300000 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
416408000 1
2.0%
1536850000 1
2.0%
1562900000 1
2.0%
1764740000 1
2.0%
1843190000 1
2.0%
2054090000 1
2.0%
2146920000 1
2.0%
2246040000 1
2.0%
2342410000 1
2.0%
2400650000 1
2.0%
ValueCountFrequency (%)
9559350000 1
2.0%
9424340000 1
2.0%
8926830000 1
2.0%
8871450000 1
2.0%
8665950000 1
2.0%
8218350000 1
2.0%
8001820000 1
2.0%
7823060000 1
2.0%
6716150000 1
2.0%
6682760000 1
2.0%

NOX
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56694678
Minimum1174170
Maximum1.50223 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:06.510242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1174170
5-th percentile5394002
Q129455500
median52599900
Q384401500
95-th percentile1.26228 × 108
Maximum1.50223 × 108
Range1.4904883 × 108
Interquartile range (IQR)54946000

Descriptive statistics

Standard deviation38398354
Coefficient of variation (CV)0.67728321
Kurtosis-0.41621719
Mean56694678
Median Absolute Deviation (MAD)31578300
Skewness0.5045263
Sum2.7780392 × 109
Variance1.4744336 × 1015
MonotonicityNot monotonic
2023-12-10T23:32:07.040055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
84401500 1
 
2.0%
42129400 1
 
2.0%
108084000 1
 
2.0%
5563820 1
 
2.0%
6647810 1
 
2.0%
120344000 1
 
2.0%
6262710 1
 
2.0%
34703000 1
 
2.0%
30595600 1
 
2.0%
73626600 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1174170 1
2.0%
4531640 1
2.0%
5280790 1
2.0%
5563820 1
2.0%
6262710 1
2.0%
6647810 1
2.0%
12379400 1
2.0%
13522500 1
2.0%
13764300 1
2.0%
17223700 1
2.0%
ValueCountFrequency (%)
150223000 1
2.0%
135625000 1
2.0%
128506000 1
2.0%
122811000 1
2.0%
120344000 1
2.0%
108084000 1
2.0%
99824900 1
2.0%
90233200 1
2.0%
87749200 1
2.0%
87723800 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
2023-12-10T23:32:07.314900image/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
2023-12-10T23:32:07.765700image/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_LOADDTI_RSTCTOT_RSTCRL_POWERFUEL_CNSMP_QTYNOXRN
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28963176332542028FduvwhqPdhuvnContainer42.8331.524.1Pdhuvn14.5RghqvhOlqgr200011038126-Jan-2023 12:00:0030-Apr-2023 18:00:00-18.139601160.35699540.8423164.97300758871.4253504.006682760000998249004
38963178332421810FruqholxvPdhuvnContainer42.0331.524.1Pdhuvn14.5RghqvhOlqgr200011038101-Apr-2023 12:00:0030-Apr-2023 18:00:0043.6073-166.69200123.7103-112.83200111561.9102483.002696750000402442005
45423663332789080PdhuvnHglqexujkContainer48.2350.029.9PlqvkhqjIlqdqfldo16.0KbxqgdlKLXovdq201014872301-Jan-2023 12:00:0030-Apr-2023 18:00:0034.936901-123.30699920.509899114.10099885934.3312156.0082183500001285060006
55423673332013778PdqfkhvwhuPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201819032601-Jan-2023 12:00:0030-Apr-2023 18:00:0036.01369916.42130136.84241.1042342145.1239312.006020320000199272007
65423693332013780PxufldPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201819032601-Jan-2023 12:00:0030-Apr-2023 18:00:0035.076801128.80499336.23615.204254884.8393892.009559350000318769008
75423613332013792PdqlodPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201819032601-Jan-2023 12:00:0030-Apr-2023 18:00:0030.4669122.7519995.4884587.03600324495.3163862.004255820000137643009
85423623332013704PxpedlPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201819032601-Jan-2023 12:00:0030-Apr-2023 18:00:0030.101999123.5729.8062122.16000454598.2360307.0088714500002945550010
95423783332013716PddvwulfkwPdhuvnContainer58.6378.033.2316.5GdhzrrGVPH201919032601-Jan-2023 12:00:0030-Apr-2023 18:00:0057.50999811.393535.5853121.31099747267.3176655.0052052900001722370011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOADDTI_RSTCTOT_RSTCRL_POWERFUEL_CNSMP_QTYNOXRN
395425563332689450PdhuvnQhzsruwContainer29.8198.716.4Pdhuvn11.2Yronvzhuiw20083462201-Jan-2023 12:00:0030-Apr-2023 18:00:0041.1147992.3023240.961728.672621387.872899.8022460400003432200041
405425583332689462PdhuvnQruironContainer29.8198.716.4Pdhuvn11.2Yronvzhuiw20083379601-Jan-2023 12:00:0030-Apr-2023 18:00:0034.21939831.565144.10369928.65979495.3342591.6015368500002185700042
415425513332685337PdhuvnVwhsqlfdContainer42.8319.024.6Pdhuvn15.0KbxqgdlKLXovdq200810797801-Jan-2023 12:00:0030-Apr-2023 18:00:0025.9191121.334999-26.413799-71.73739635013.7123720.0037017200005759160043
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435425643332685351PdhuvnVdydqqdkContainer42.8319.024.6Pdhuvn15.0KbxqgdlKLXovdq200810797801-Jan-2023 12:00:0030-Apr-2023 18:00:003.05562-86.73709929.9867122.82199969550.7330558.00782306000012281100045
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465426373332155198WxnxpdDufwlfdContainer31.0176.217.4UrbdoDufwlfOlqh12.0KxdqjsxZhqfkrqj20202566801-Jan-2023 12:00:0030-Apr-2023 18:00:0057.43489810.553359.329201-44.521560478.8133248.0037366100001237940048
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485426973332941494QDQRTDUFWLFDCargo or Containership15.674.26.2QD4.5QD2020270001-Jan-2023 12:00:0030-Apr-2023 18:00:0064.167503-51.71630161.812099-49.7210013020.666737.180416408000117417050