Overview

Dataset statistics

Number of variables22
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory194.7 B

Variable types

Numeric16
Text1
Categorical5

Dataset

DescriptionSample
Author올시데이터
URLhttp://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001330

Alerts

DPTR_HMS is highly imbalanced (73.5%)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
PRFMC has unique valuesUnique
FUEL_CNSMP_QTY has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-09-25 08:59:11.178207
Analysis finished2023-09-25 08:59:11.566096
Duration0.39 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%
Mean6.5957018 × 108
Minimum5.0654966 × 108
Maximum9.6934192 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:11.734584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.0654966 × 108
5-th percentile5.0671267 × 108
Q15.4559033 × 108
median6.0723933 × 108
Q37.4655315 × 108
95-th percentile8.9633789 × 108
Maximum9.6934192 × 108
Range4.6279225 × 108
Interquartile range (IQR)2.0096282 × 108

Descriptive statistics

Standard deviation1.3084504 × 108
Coefficient of variation (CV)0.19837925
Kurtosis-0.26474814
Mean6.5957018 × 108
Median Absolute Deviation (MAD)63934000
Skewness0.89578715
Sum3.2318939 × 1010
Variance1.7120424 × 1016
MonotonicityNot monotonic
2023-09-25T17:59:12.148840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
577894333 1
 
2.0%
543305333 1
 
2.0%
866465475 1
 
2.0%
858348258 1
 
2.0%
577153388 1
 
2.0%
604489333 1
 
2.0%
588138116 1
 
2.0%
543964333 1
 
2.0%
773697333 1
 
2.0%
607239333 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
506549663 1
2.0%
506622873 1
2.0%
506686133 1
2.0%
506752473 1
2.0%
543305333 1
2.0%
543690333 1
2.0%
543964333 1
2.0%
544519773 1
2.0%
544567713 1
2.0%
544598863 1
2.0%
ValueCountFrequency (%)
969341917 1
2.0%
969325622 1
2.0%
896337933 1
2.0%
896337833 1
2.0%
866465475 1
2.0%
858458345 1
2.0%
858348258 1
2.0%
858346376 1
2.0%
807335453 1
2.0%
774706333 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2412791
Minimum1034361
Maximum2983715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:12.411300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1034361
5-th percentile1242627.8
Q12374873
median2474374
Q32642828
95-th percentile2944380.2
Maximum2983715
Range1949354
Interquartile range (IQR)267955

Descriptive statistics

Standard deviation442913.65
Coefficient of variation (CV)0.18356901
Kurtosis2.9350259
Mean2412791
Median Absolute Deviation (MAD)164781
Skewness-1.6808943
Sum1.1822676 × 108
Variance1.961725 × 1011
MonotonicityNot monotonic
2023-09-25T17:59:12.676203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2608468 1
 
2.0%
2698217 1
 
2.0%
2464393 1
 
2.0%
2972821 1
 
2.0%
2825822 1
 
2.0%
2374873 1
 
2.0%
2431396 1
 
2.0%
2309593 1
 
2.0%
2402785 1
 
2.0%
2473514 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1034361 1
2.0%
1231309 1
2.0%
1240805 1
2.0%
1245362 1
2.0%
1832892 1
2.0%
2029686 1
2.0%
2069612 1
2.0%
2074215 1
2.0%
2154869 1
2.0%
2238902 1
2.0%
ValueCountFrequency (%)
2983715 1
2.0%
2972821 1
2.0%
2944381 1
2.0%
2944379 1
2.0%
2921614 1
2.0%
2825822 1
2.0%
2766777 1
2.0%
2762707 1
2.0%
2698217 1
2.0%
2680715 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-09-25T17:59:13.140285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length10.326531
Min length4

Characters and Unicode

Total characters506
Distinct characters53
Distinct categories3 ?
Distinct scripts2 ?
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 rowYDQTXLVK
2nd rowFRVFRNlnx
3rd rowVWDUFRPHW
4th rowFUBVWDODWODQWLFD
5th rowDWODQWLFKRULCRQ
ValueCountFrequency (%)
ydqtxlvk 1
 
2.0%
odnhshduo 1
 
2.0%
pwwvlqjdsruh 1
 
2.0%
sxodxqxqxndq 1
 
2.0%
urhueruj 1
 
2.0%
kxdix530 1
 
2.0%
slulwd 1
 
2.0%
iulriruzlq 1
 
2.0%
vlqrnruyodglyrvwrn 1
 
2.0%
vklqixml 1
 
2.0%
Other values (39) 39
79.6%
2023-09-25T17:59:14.213115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 44
 
8.7%
Q 29
 
5.7%
L 26
 
5.1%
H 26
 
5.1%
R 24
 
4.7%
U 23
 
4.5%
W 22
 
4.3%
F 20
 
4.0%
d 19
 
3.8%
V 18
 
3.6%
Other values (43) 255
50.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 360
71.1%
Lowercase Letter 138
 
27.3%
Decimal Number 8
 
1.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 44
 
12.2%
Q 29
 
8.1%
L 26
 
7.2%
H 26
 
7.2%
R 24
 
6.7%
U 23
 
6.4%
W 22
 
6.1%
F 20
 
5.6%
V 18
 
5.0%
K 14
 
3.9%
Other values (16) 114
31.7%
Lowercase Letter
ValueCountFrequency (%)
d 19
13.8%
q 14
10.1%
r 14
10.1%
l 11
 
8.0%
h 10
 
7.2%
w 9
 
6.5%
o 8
 
5.8%
u 7
 
5.1%
v 7
 
5.1%
x 6
 
4.3%
Other values (12) 33
23.9%
Decimal Number
ValueCountFrequency (%)
5 4
50.0%
4 1
 
12.5%
6 1
 
12.5%
3 1
 
12.5%
0 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 498
98.4%
Common 8
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 44
 
8.8%
Q 29
 
5.8%
L 26
 
5.2%
H 26
 
5.2%
R 24
 
4.8%
U 23
 
4.6%
W 22
 
4.4%
F 20
 
4.0%
d 19
 
3.8%
V 18
 
3.6%
Other values (38) 247
49.6%
Common
ValueCountFrequency (%)
5 4
50.0%
4 1
 
12.5%
6 1
 
12.5%
3 1
 
12.5%
0 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 44
 
8.7%
Q 29
 
5.7%
L 26
 
5.1%
H 26
 
5.1%
R 24
 
4.7%
U 23
 
4.5%
W 22
 
4.3%
F 20
 
4.0%
d 19
 
3.8%
V 18
 
3.6%
Other values (43) 255
50.4%

SHIP_KIND
Categorical

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
Container
19 
Container(reefer)
13 
Cargo or Containership
9 
Container(add)
8 

Length

Max length22
Median length17
Mean length14.326531
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCargo or Containership
2nd rowContainer(add)
3rd rowContainer
4th rowContainer(reefer)
5th rowCargo or Containership

Common Values

ValueCountFrequency (%)
Container 19
38.8%
Container(reefer) 13
26.5%
Cargo or Containership 9
18.4%
Container(add) 8
16.3%

Length

2023-09-25T17:59:14.538532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-25T17:59:14.725802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
container 19
28.4%
container(reefer 13
19.4%
cargo 9
13.4%
or 9
13.4%
containership 9
13.4%
container(add 8
11.9%

SHIP_WDTH
Real number (ℝ)

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.585714
Minimum14
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:15.052588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.2
Q117.9
median19.7
Q322
95-th percentile23
Maximum24
Range10
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation2.76217
Coefficient of variation (CV)0.14102983
Kurtosis-0.76789466
Mean19.585714
Median Absolute Deviation (MAD)2.3
Skewness-0.41179486
Sum959.7
Variance7.6295833
MonotonicityNot monotonic
2023-09-25T17:59:15.318274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
22.0 4
 
8.2%
22.6 4
 
8.2%
23.0 3
 
6.1%
14.0 3
 
6.1%
16.6 2
 
4.1%
18.0 2
 
4.1%
20.0 2
 
4.1%
17.0 2
 
4.1%
15.9 2
 
4.1%
18.2 2
 
4.1%
Other values (19) 23
46.9%
ValueCountFrequency (%)
14.0 3
6.1%
14.5 1
 
2.0%
15.9 2
4.1%
16.4 1
 
2.0%
16.6 2
4.1%
17.0 2
4.1%
17.8 1
 
2.0%
17.9 1
 
2.0%
18.0 2
4.1%
18.2 2
4.1%
ValueCountFrequency (%)
24.0 1
 
2.0%
23.3 1
 
2.0%
23.0 3
6.1%
22.6 4
8.2%
22.5 1
 
2.0%
22.2 2
4.1%
22.0 4
8.2%
21.7 1
 
2.0%
21.0 2
4.1%
20.8 1
 
2.0%

SHIP_LNTH
Real number (ℝ)

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.28367
Minimum89.8
Maximum166.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:15.633072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89.8
5-th percentile99.44
Q1119.2
median129.6
Q3133.4
95-th percentile142
Maximum166.1
Range76.3
Interquartile range (IQR)14.2

Descriptive statistics

Standard deviation14.961845
Coefficient of variation (CV)0.11942374
Kurtosis0.72615087
Mean125.28367
Median Absolute Deviation (MAD)8.6
Skewness-0.4546734
Sum6138.9
Variance223.85681
MonotonicityNot monotonic
2023-09-25T17:59:16.106798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
133.4 3
 
6.1%
120.8 3
 
6.1%
100.0 2
 
4.1%
110.8 2
 
4.1%
133.0 2
 
4.1%
142.0 2
 
4.1%
129.0 1
 
2.0%
134.0 1
 
2.0%
125.0 1
 
2.0%
99.5 1
 
2.0%
Other values (31) 31
63.3%
ValueCountFrequency (%)
89.8 1
2.0%
91.5 1
2.0%
99.4 1
2.0%
99.5 1
2.0%
100.0 2
4.1%
101.1 1
2.0%
110.8 2
4.1%
115.0 1
2.0%
118.0 1
2.0%
118.3 1
2.0%
ValueCountFrequency (%)
166.1 1
2.0%
144.6 1
2.0%
142.0 2
4.1%
140.3 1
2.0%
140.1 1
2.0%
138.2 1
2.0%
137.6 1
2.0%
135.0 1
2.0%
134.0 1
2.0%
133.6 1
2.0%

SHIP_HGHT
Real number (ℝ)

Distinct36
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.165306
Minimum6.5
Maximum13.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:16.367961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile7.26
Q18.9
median10.1
Q311.3
95-th percentile13.52
Maximum13.8
Range7.3
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation1.8514129
Coefficient of variation (CV)0.18213056
Kurtosis-0.48185562
Mean10.165306
Median Absolute Deviation (MAD)1.2
Skewness0.11053299
Sum498.1
Variance3.4277296
MonotonicityNot monotonic
2023-09-25T17:59:16.595808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
11.3 5
 
10.2%
9.8 3
 
6.1%
9.5 2
 
4.1%
11.2 2
 
4.1%
10.8 2
 
4.1%
8.0 2
 
4.1%
13.6 2
 
4.1%
9.1 2
 
4.1%
10.2 2
 
4.1%
7.7 1
 
2.0%
Other values (26) 26
53.1%
ValueCountFrequency (%)
6.5 1
2.0%
6.9 1
2.0%
7.1 1
2.0%
7.5 1
2.0%
7.7 1
2.0%
7.8 1
2.0%
7.9 1
2.0%
8.0 2
4.1%
8.5 1
2.0%
8.6 1
2.0%
ValueCountFrequency (%)
13.8 1
 
2.0%
13.6 2
 
4.1%
13.4 1
 
2.0%
13.3 1
 
2.0%
13.0 1
 
2.0%
11.9 1
 
2.0%
11.8 1
 
2.0%
11.5 1
 
2.0%
11.4 1
 
2.0%
11.3 5
10.2%

SHIP_OWNER_NM
Categorical

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
QD
35 
3
8 
QruvhVklskroglqj
 
2
MdfnvrrqVklsslqj
 
1
YlqdolqhvVklsslqj
 
1
Other values (2)
 
2

Length

Max length17
Median length2
Mean length3.3061224
Min length1

Unique

Unique4 ?
Unique (%)8.2%

Sample

1st rowQD
2nd row3
3rd rowQD
4th rowQD
5th rowQD

Common Values

ValueCountFrequency (%)
QD 35
71.4%
3 8
 
16.3%
QruvhVklskroglqj 2
 
4.1%
MdfnvrrqVklsslqj 1
 
2.0%
YlqdolqhvVklsslqj 1
 
2.0%
VLSJ 1
 
2.0%
QlqjerRfhdqVksj 1
 
2.0%

Length

2023-09-25T17:59:16.865851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-25T17:59:17.080597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
qd 35
71.4%
3 8
 
16.3%
qruvhvklskroglqj 2
 
4.1%
mdfnvrrqvklsslqj 1
 
2.0%
ylqdolqhvvklsslqj 1
 
2.0%
vlsj 1
 
2.0%
qlqjerrfhdqvksj 1
 
2.0%

DRAFT
Real number (ℝ)

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4673469
Minimum4.6
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:17.277851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile5.58
Q16.9
median7.4
Q38.4
95-th percentile9.64
Maximum9.8
Range5.2
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.2344681
Coefficient of variation (CV)0.16531549
Kurtosis-0.22685416
Mean7.4673469
Median Absolute Deviation (MAD)1
Skewness-0.027995821
Sum365.9
Variance1.5239116
MonotonicityNot monotonic
2023-09-25T17:59:17.484946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8.4 7
14.3%
7.1 6
12.2%
7.4 6
12.2%
6.0 3
 
6.1%
9.8 3
 
6.1%
7.6 3
 
6.1%
7.2 2
 
4.1%
6.5 2
 
4.1%
9.4 2
 
4.1%
7.3 2
 
4.1%
Other values (13) 13
26.5%
ValueCountFrequency (%)
4.6 1
 
2.0%
5.0 1
 
2.0%
5.5 1
 
2.0%
5.7 1
 
2.0%
5.8 1
 
2.0%
6.0 3
6.1%
6.2 1
 
2.0%
6.5 2
4.1%
6.7 1
 
2.0%
6.9 1
 
2.0%
ValueCountFrequency (%)
9.8 3
6.1%
9.4 2
 
4.1%
9.1 1
 
2.0%
8.8 1
 
2.0%
8.7 1
 
2.0%
8.6 1
 
2.0%
8.4 7
14.3%
7.6 3
6.1%
7.5 1
 
2.0%
7.4 6
12.2%

SHPYRD_NM
Categorical

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
QD
35 
WdlckrxNrxdqVE
 
3
NbrnxbrVklsbdug
 
2
CkhmldqjBdqjidq
 
2
PdzhlVEPdzhl
 
1
Other values (6)
6 

Length

Max length17
Median length2
Mean length5.3265306
Min length2

Unique

Unique7 ?
Unique (%)14.3%

Sample

1st rowQD
2nd rowNbrnxbrVklsbdug
3rd rowQD
4th rowQD
5th rowQD

Common Values

ValueCountFrequency (%)
QD 35
71.4%
WdlckrxNrxdqVE 3
 
6.1%
NbrnxbrVklsbdug 2
 
4.1%
CkhmldqjBdqjidq 2
 
4.1%
PdzhlVEPdzhl 1
 
2.0%
QdydoJlmrq 1
 
2.0%
KKLFBhrqjgr 1
 
2.0%
VkdqgrqjZhlkdlVB 1
 
2.0%
KxdqjkdlVE 1
 
2.0%
LKGDVklsexlog 1
 
2.0%

Length

2023-09-25T17:59:17.716825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qd 35
71.4%
wdlckrxnrxdqve 3
 
6.1%
nbrnxbrvklsbdug 2
 
4.1%
ckhmldqjbdqjidq 2
 
4.1%
pdzhlvepdzhl 1
 
2.0%
qdydojlmrq 1
 
2.0%
kklfbhrqjgr 1
 
2.0%
vkdqgrqjzhlkdlvb 1
 
2.0%
kxdqjkdlve 1
 
2.0%
lkgdvklsexlog 1
 
2.0%

BULD_YR
Real number (ℝ)

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.898
Minimum1987
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:17.952246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile1991
Q11996
median2003
Q32009
95-th percentile2016
Maximum2019
Range32
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.7136026
Coefficient of variation (CV)0.0043504975
Kurtosis-1.1178642
Mean2002.898
Median Absolute Deviation (MAD)7
Skewness0.09181455
Sum98142
Variance75.926871
MonotonicityNot monotonic
2023-09-25T17:59:18.180729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1996 5
 
10.2%
2007 5
 
10.2%
1991 4
 
8.2%
1998 4
 
8.2%
2002 3
 
6.1%
1995 3
 
6.1%
2003 2
 
4.1%
2015 2
 
4.1%
2016 2
 
4.1%
2012 2
 
4.1%
Other values (14) 17
34.7%
ValueCountFrequency (%)
1987 1
 
2.0%
1989 1
 
2.0%
1991 4
8.2%
1992 1
 
2.0%
1994 1
 
2.0%
1995 3
6.1%
1996 5
10.2%
1997 1
 
2.0%
1998 4
8.2%
2002 3
6.1%
ValueCountFrequency (%)
2019 1
2.0%
2018 1
2.0%
2016 2
4.1%
2015 2
4.1%
2014 1
2.0%
2013 2
4.1%
2012 2
4.1%
2011 1
2.0%
2009 1
2.0%
2008 2
4.1%

DDWGHT
Real number (ℝ)

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9000.5714
Minimum3050
Maximum23000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:18.755266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3050
5-th percentile4195.2
Q16665
median8464
Q311095
95-th percentile13540.6
Maximum23000
Range19950
Interquartile range (IQR)4430

Descriptive statistics

Standard deviation3547.7938
Coefficient of variation (CV)0.39417429
Kurtosis3.6231301
Mean9000.5714
Median Absolute Deviation (MAD)2567
Skewness1.1877713
Sum441028
Variance12586841
MonotonicityNot monotonic
2023-09-25T17:59:19.022087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
13109 2
 
4.1%
11067 2
 
4.1%
4800 1
 
2.0%
11095 1
 
2.0%
23000 1
 
2.0%
7368 1
 
2.0%
7946 1
 
2.0%
7661 1
 
2.0%
11031 1
 
2.0%
3966 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
3050 1
2.0%
3728 1
2.0%
3966 1
2.0%
4539 1
2.0%
4800 1
2.0%
5020 1
2.0%
5202 1
2.0%
5220 1
2.0%
5249 1
2.0%
6090 1
2.0%
ValueCountFrequency (%)
23000 1
2.0%
14537 1
2.0%
13719 1
2.0%
13273 1
2.0%
13109 2
4.1%
12587 1
2.0%
12004 1
2.0%
11865 1
2.0%
11855 1
2.0%
11500 1
2.0%

DPTR_HMS
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2023 12:00:00
45 
02-Jan-2023 00:00:00
 
2
01-Apr-2023 12:00:00
 
1
01-Jan-2023 18:00:00
 
1

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique2 ?
Unique (%)4.1%

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 45
91.8%
02-Jan-2023 00:00:00 2
 
4.1%
01-Apr-2023 12:00:00 1
 
2.0%
01-Jan-2023 18:00:00 1
 
2.0%

Length

2023-09-25T17:59:19.323286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-25T17:59:19.547085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
01-jan-2023 46
46.9%
12:00:00 46
46.9%
02-jan-2023 2
 
2.0%
00:00:00 2
 
2.0%
01-apr-2023 1
 
1.0%
18:00:00 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 06:00:00
 
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 06:00:00 1
 
2.0%

Length

2023-09-25T17:59:19.746327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-25T17:59:19.909932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30-apr-2023 49
50.0%
18:00:00 48
49.0%
06:00:00 1
 
1.0%

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.896521
Minimum-15.8018
Maximum63.3167
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)16.3%
Memory size573.0 B
2023-09-25T17:59:20.177036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-15.8018
5-th percentile-7.514974
Q113.3543
median34.956699
Q349.5728
95-th percentile59.44412
Maximum63.3167
Range79.1185
Interquartile range (IQR)36.2185

Descriptive statistics

Standard deviation22.815919
Coefficient of variation (CV)0.76316301
Kurtosis-1.023119
Mean29.896521
Median Absolute Deviation (MAD)18.522801
Skewness-0.34389341
Sum1464.9295
Variance520.56616
MonotonicityNot monotonic
2023-09-25T17:59:20.452450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
26.131701 1
 
2.0%
49.5728 1
 
2.0%
10.2782 1
 
2.0%
-6.02965 1
 
2.0%
44.429298 1
 
2.0%
2.48887 1
 
2.0%
58.918701 1
 
2.0%
-8.78345 1
 
2.0%
35.064301 1
 
2.0%
6.03386 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-15.8018 1
2.0%
-8.78345 1
2.0%
-8.50519 1
2.0%
-6.02965 1
2.0%
-5.36923 1
2.0%
-3.60882 1
2.0%
-2.33667 1
2.0%
-1.78664 1
2.0%
2.48887 1
2.0%
4.24625 1
2.0%
ValueCountFrequency (%)
63.3167 1
2.0%
63.220798 1
2.0%
59.794399 1
2.0%
58.918701 1
2.0%
57.748901 1
2.0%
56.981899 1
2.0%
56.350201 1
2.0%
55.986401 1
2.0%
55.853001 1
2.0%
55.7136 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.460308
Minimum-162.535
Maximum179.176
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)32.7%
Memory size573.0 B
2023-09-25T17:59:20.771398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-162.535
5-th percentile-92.061319
Q1-5.97227
median96.216698
Q3123.193
95-th percentile148.1142
Maximum179.176
Range341.711
Interquartile range (IQR)129.16527

Descriptive statistics

Standard deviation87.284194
Coefficient of variation (CV)1.7647321
Kurtosis-0.86339295
Mean49.460308
Median Absolute Deviation (MAD)73.040306
Skewness-0.51065362
Sum2423.5551
Variance7618.5304
MonotonicityNot monotonic
2023-09-25T17:59:21.038037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-80.063301 1
 
2.0%
-4.02166 1
 
2.0%
96.216698 1
 
2.0%
106.912003 1
 
2.0%
-68.904503 1
 
2.0%
131.565002 1
 
2.0%
5.58618 1
 
2.0%
13.2767 1
 
2.0%
128.794006 1
 
2.0%
125.144997 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-162.535004 1
2.0%
-106.678001 1
2.0%
-94.5 1
2.0%
-88.403297 1
2.0%
-80.063301 1
2.0%
-79.959099 1
2.0%
-68.904503 1
2.0%
-68.173103 1
2.0%
-33.042801 1
2.0%
-20.775999 1
2.0%
ValueCountFrequency (%)
179.175995 1
2.0%
169.257004 1
2.0%
151.707001 1
2.0%
142.725006 1
2.0%
131.878998 1
2.0%
131.565002 1
2.0%
129.371994 1
2.0%
129.007004 1
2.0%
129.001999 1
2.0%
128.794006 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.648385
Minimum-8.69617
Maximum59.333698
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)12.2%
Memory size573.0 B
2023-09-25T17:59:21.318515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.69617
5-th percentile-6.800982
Q113.3587
median34.327099
Q350.254101
95-th percentile54.667121
Maximum59.333698
Range68.029868
Interquartile range (IQR)36.895401

Descriptive statistics

Standard deviation20.956572
Coefficient of variation (CV)0.68377412
Kurtosis-0.93322081
Mean30.648385
Median Absolute Deviation (MAD)16.1901
Skewness-0.53876908
Sum1501.7708
Variance439.17791
MonotonicityNot monotonic
2023-09-25T17:59:21.556309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
20.477699 1
 
2.0%
50.675098 1
 
2.0%
2.81333 1
 
2.0%
-5.66676 1
 
2.0%
44.387699 1
 
2.0%
13.3587 1
 
2.0%
51.881802 1
 
2.0%
-8.69617 1
 
2.0%
33.925999 1
 
2.0%
5.8414 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-8.69617 1
2.0%
-8.49991 1
2.0%
-7.18719 1
2.0%
-6.22167 1
2.0%
-6.03633 1
2.0%
-5.66676 1
2.0%
2.81333 1
2.0%
5.8414 1
2.0%
6.98793 1
2.0%
7.64226 1
2.0%
ValueCountFrequency (%)
59.333698 1
2.0%
57.584999 1
2.0%
54.674801 1
2.0%
54.655602 1
2.0%
54.4067 1
2.0%
53.628502 1
2.0%
53.5355 1
2.0%
53.4188 1
2.0%
52.7766 1
2.0%
51.881802 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.666745
Minimum-94.413696
Maximum179.18201
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)24.5%
Memory size573.0 B
2023-09-25T17:59:21.819363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-94.413696
5-th percentile-89.56738
Q11.28696
median101.278
Q3129.071
95-th percentile157.6054
Maximum179.18201
Range273.5957
Interquartile range (IQR)127.78404

Descriptive statistics

Standard deviation80.00814
Coefficient of variation (CV)1.276724
Kurtosis-0.97441846
Mean62.666745
Median Absolute Deviation (MAD)56.875
Skewness-0.48366861
Sum3070.6705
Variance6401.3024
MonotonicityNot monotonic
2023-09-25T17:59:22.103758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-86.347603 1
 
2.0%
1.28696 1
 
2.0%
101.278 1
 
2.0%
108.764999 1
 
2.0%
-36.048698 1
 
2.0%
100.630997 1
 
2.0%
4.43686 1
 
2.0%
13.2985 1
 
2.0%
130.889999 1
 
2.0%
125.434998 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-94.413696 1
2.0%
-93.261002 1
2.0%
-91.713898 1
2.0%
-86.347603 1
2.0%
-79.928902 1
2.0%
-36.048698 1
2.0%
-10.0601 1
2.0%
-6.53154 1
2.0%
-3.61352 1
2.0%
-0.451635 1
2.0%
ValueCountFrequency (%)
179.182007 1
2.0%
177.145996 1
2.0%
158.153 1
2.0%
156.783997 1
2.0%
148.509003 1
2.0%
144.246994 1
2.0%
134.472 1
2.0%
133.098999 1
2.0%
132.854004 1
2.0%
130.889999 1
2.0%

PRFMC
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63277094
Minimum59813500
Maximum97192000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:22.384828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59813500
5-th percentile59922040
Q160741700
median61372800
Q361805200
95-th percentile81330720
Maximum97192000
Range37378500
Interquartile range (IQR)1063500

Descriptive statistics

Standard deviation8514261.2
Coefficient of variation (CV)0.13455519
Kurtosis12.700755
Mean63277094
Median Absolute Deviation (MAD)521300
Skewness3.7446395
Sum3.1005776 × 109
Variance7.2492644 × 1013
MonotonicityStrictly increasing
2023-09-25T17:59:22.655616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
59813500 1
 
2.0%
61842900 1
 
2.0%
61506900 1
 
2.0%
61565100 1
 
2.0%
61619300 1
 
2.0%
61648200 1
 
2.0%
61649000 1
 
2.0%
61662300 1
 
2.0%
61666600 1
 
2.0%
61771600 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
59813500 1
2.0%
59900800 1
2.0%
59905800 1
2.0%
59946400 1
2.0%
59955900 1
2.0%
60041000 1
2.0%
60087000 1
2.0%
60119400 1
2.0%
60370100 1
2.0%
60410600 1
2.0%
ValueCountFrequency (%)
97192000 1
2.0%
97183200 1
2.0%
94013000 1
2.0%
62307300 1
2.0%
62180600 1
2.0%
62147300 1
2.0%
62107400 1
2.0%
62013800 1
2.0%
61852700 1
2.0%
61850200 1
2.0%

FUEL_CNSMP_QTY
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9521563 × 108
Minimum1.03938 × 108
Maximum2.71183 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:22.898325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.03938 × 108
5-th percentile3.521762 × 108
Q14.85704 × 108
median7.01514 × 108
Q39.73295 × 108
95-th percentile1.544742 × 109
Maximum2.71183 × 109
Range2.607892 × 109
Interquartile range (IQR)4.87591 × 108

Descriptive statistics

Standard deviation4.5797252 × 108
Coefficient of variation (CV)0.57590986
Kurtosis5.2364025
Mean7.9521563 × 108
Median Absolute Deviation (MAD)2.35711 × 108
Skewness1.8038181
Sum3.8965566 × 1010
Variance2.0973883 × 1017
MonotonicityNot monotonic
2023-09-25T17:59:23.600600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
542914000 1
 
2.0%
1244940000 1
 
2.0%
800488000 1
 
2.0%
354656000 1
 
2.0%
1120380000 1
 
2.0%
533503000 1
 
2.0%
705936000 1
 
2.0%
485704000 1
 
2.0%
1083580000 1
 
2.0%
413488000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
103938000 1
2.0%
320335000 1
2.0%
350523000 1
2.0%
354656000 1
2.0%
358693000 1
2.0%
363357000 1
2.0%
372735000 1
2.0%
388315000 1
2.0%
413488000 1
2.0%
413532000 1
2.0%
ValueCountFrequency (%)
2711830000 1
2.0%
1694960000 1
2.0%
1648650000 1
2.0%
1388880000 1
2.0%
1337800000 1
2.0%
1326500000 1
2.0%
1271630000 1
2.0%
1244940000 1
2.0%
1211680000 1
2.0%
1120380000 1
2.0%

NVGTN_DIST
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.351727
Minimum1.71913
Maximum27.9043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-09-25T17:59:23.807776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.71913
5-th percentile5.679694
Q17.89696
median11.3021
Q315.4881
95-th percentile22.66844
Maximum27.9043
Range26.18517
Interquartile range (IQR)7.59114

Descriptive statistics

Standard deviation6.0034252
Coefficient of variation (CV)0.48603933
Kurtosis0.18108764
Mean12.351727
Median Absolute Deviation (MAD)3.42526
Skewness0.8130628
Sum605.23463
Variance36.041114
MonotonicityNot monotonic
2023-09-25T17:59:23.992095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9.07678 1
 
2.0%
20.1307 1
 
2.0%
13.0146 1
 
2.0%
5.76067 1
 
2.0%
18.1823 1
 
2.0%
8.65399 1
 
2.0%
11.4509 1
 
2.0%
7.87684 1
 
2.0%
17.5716 1
 
2.0%
6.69382 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1.71913 1
2.0%
5.2669 1
2.0%
5.62571 1
2.0%
5.76067 1
2.0%
5.88164 1
2.0%
5.96333 1
2.0%
6.21682 1
2.0%
6.45906 1
2.0%
6.69382 1
2.0%
6.79604 1
2.0%
ValueCountFrequency (%)
27.9043 1
2.0%
27.523 1
2.0%
23.1322 1
2.0%
21.9728 1
2.0%
21.8522 1
2.0%
20.5615 1
2.0%
20.1307 1
2.0%
19.4969 1
2.0%
18.1823 1
2.0%
18.029 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
2023-09-25T17:59:24.202294image/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
2023-09-25T17:59:24.417991image/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

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45450053332674077DWODQWLFKRULCRQCargo or Containership14.0110.88.5QD6.5QD2006609001-Jan-2023 12:00:0030-Apr-2023 18:00:0033.173302-8.6682543.7444-6.53154599559003727350006.216826
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88963379332944381NdodpdcrrContainer22.6133.411.3QruvhVklskroglqj8.4WdlckrxNrxdqVE20131310901-Jan-2023 12:00:0030-Apr-2023 18:00:0020.9734-162.53500434.327099129.07099960370100132650000021.972810
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MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
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425452933332641252TXHHQEContainer19.5132.69.9QD7.1QD2004806101-Jan-2023 12:00:0030-Apr-2023 06:00:0018.330799-94.518.136999-94.4136966210740084433200013.594744
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445770830492983715HAHERUJCargo or Containership15.9138.211.2QD7.6QD20131200401-Jan-2023 12:00:0030-Apr-2023 18:00:0056.350201-19.32941.802601-10.06016218060089578000014.406146
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