Overview

Dataset statistics

Number of variables35
Number of observations49
Missing cells50
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 KiB
Average record size in memory311.7 B

Variable types

Numeric13
Text2
Categorical20

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000424

Alerts

SHIP_OWNER_NM has constant value ""Constant
WAVE_AVE_VE_9M has constant value ""Constant
WAVE_MAX_VE_9M has constant value ""Constant
SHIP_HGHT is highly imbalanced (69.2%)Imbalance
DRAFT is highly imbalanced (75.4%)Imbalance
SHPYRD_NM is highly imbalanced (60.6%)Imbalance
DPTR_HMS is highly imbalanced (60.0%)Imbalance
ARVL_HMS is highly imbalanced (51.6%)Imbalance
WAVE_AVE_VE_4M is highly imbalanced (75.4%)Imbalance
WAVE_AVE_VE_5M is highly imbalanced (85.6%)Imbalance
WAVE_AVE_VE_6M is highly imbalanced (85.6%)Imbalance
WAVE_AVE_VE_7M is highly imbalanced (85.6%)Imbalance
WAVE_AVE_VE_8M is highly imbalanced (85.6%)Imbalance
WAVE_AVE_VE_10M_ABOVE is highly imbalanced (85.6%)Imbalance
WAVE_MAX_VE_4M is highly imbalanced (75.4%)Imbalance
WAVE_MAX_VE_5M is highly imbalanced (85.6%)Imbalance
WAVE_MAX_VE_6M is highly imbalanced (85.6%)Imbalance
WAVE_MAX_VE_7M is highly imbalanced (85.6%)Imbalance
WAVE_MAX_VE_8M is highly imbalanced (85.6%)Imbalance
WAVE_MAX_VE_10M_ABOVE is highly imbalanced (85.6%)Imbalance
SHIP_NM has 2 (4.1%) missing valuesMissing
SHIP_OWNER_NM has 48 (98.0%) missing valuesMissing
MMSI has unique valuesUnique
WAVE_AVE_VE_1M has unique valuesUnique
WAVE_AVE_VE_2M has unique valuesUnique
WAVE_AVE_VE_3M has unique valuesUnique
WAVE_MAX_VE_1M has unique valuesUnique
WAVE_MAX_VE_3M has unique valuesUnique
RN has unique valuesUnique
IMO_IDNTF_NO has 43 (87.8%) zerosZeros
SHIP_WDTH has 2 (4.1%) zerosZeros
SHIP_LNTH has 2 (4.1%) zerosZeros
BULD_YR has 23 (46.9%) zerosZeros
DDWGHT has 28 (57.1%) zerosZeros

Reproduction

Analysis started2023-12-10 14:47:59.954454
Analysis finished2023-12-10 14:48:00.280940
Duration0.33 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%
Mean2.0549607 × 108
Minimum2.0543599 × 108
Maximum2.0552689 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:00.374374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0543599 × 108
5-th percentile2.0544337 × 108
Q12.0548129 × 108
median2.0550399 × 108
Q32.0551799 × 108
95-th percentile2.0552617 × 108
Maximum2.0552689 × 108
Range90900
Interquartile range (IQR)36700

Descriptive statistics

Standard deviation28335.61
Coefficient of variation (CV)0.00013788881
Kurtosis-0.53589357
Mean2.0549607 × 108
Median Absolute Deviation (MAD)17700
Skewness-0.86815269
Sum1.0069308 × 1010
Variance8.0290679 × 108
MonotonicityStrictly increasing
2023-12-10T23:48:00.544885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
205435990 1
 
2.0%
205521690 1
 
2.0%
205506000 1
 
2.0%
205508590 1
 
2.0%
205509390 1
 
2.0%
205509590 1
 
2.0%
205513990 1
 
2.0%
205515190 1
 
2.0%
205515590 1
 
2.0%
205515690 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
205435990 1
2.0%
205436190 1
2.0%
205442290 1
2.0%
205445000 1
2.0%
205446590 1
2.0%
205448890 1
2.0%
205452690 1
2.0%
205453690 1
2.0%
205457590 1
2.0%
205460000 1
2.0%
ValueCountFrequency (%)
205526890 1
2.0%
205526390 1
2.0%
205526290 1
2.0%
205526000 1
2.0%
205525390 1
2.0%
205524390 1
2.0%
205524290 1
2.0%
205523890 1
2.0%
205523290 1
2.0%
205522990 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1144616.2
Minimum0
Maximum9380738
Zeros43
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:00.655990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9361298.6
Maximum9380738
Range9380738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3095982.8
Coefficient of variation (CV)2.7048217
Kurtosis3.8037736
Mean1144616.2
Median Absolute Deviation (MAD)0
Skewness2.3769663
Sum56086196
Variance9.5851096 × 1012
MonotonicityNot monotonic
2023-12-10T23:48:00.750295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 43
87.8%
9322255 1
 
2.0%
9292761 1
 
2.0%
9361079 1
 
2.0%
9380738 1
 
2.0%
9361445 1
 
2.0%
9367918 1
 
2.0%
ValueCountFrequency (%)
0 43
87.8%
9292761 1
 
2.0%
9322255 1
 
2.0%
9361079 1
 
2.0%
9361445 1
 
2.0%
9367918 1
 
2.0%
9380738 1
 
2.0%
ValueCountFrequency (%)
9380738 1
 
2.0%
9367918 1
 
2.0%
9361445 1
 
2.0%
9361079 1
 
2.0%
9322255 1
 
2.0%
9292761 1
 
2.0%
0 43
87.8%

SHIP_NM
Text

MISSING 

Distinct47
Distinct (%)100.0%
Missing2
Missing (%)4.1%
Memory size524.0 B
2023-12-10T23:48:00.976703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length8.0425532
Min length5

Characters and Unicode

Total characters378
Distinct characters29
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

Unique47 ?
Unique (%)100.0%

Sample

1st rowVERANO
2nd rowPORRENA
3rd rowTARSIS
4th rowSUMMIT LNG
5th rowMTS VIVALDI
ValueCountFrequency (%)
trafuco 2
 
3.3%
7 2
 
3.3%
somtrans 2
 
3.3%
banco 1
 
1.7%
antverpia 1
 
1.7%
corylophida 1
 
1.7%
montana 1
 
1.7%
6 1
 
1.7%
explorer 1
 
1.7%
beringzee 1
 
1.7%
Other values (47) 47
78.3%
2023-12-10T23:48:01.342320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 44
11.6%
R 35
 
9.3%
E 32
 
8.5%
O 29
 
7.7%
S 29
 
7.7%
I 26
 
6.9%
T 25
 
6.6%
N 24
 
6.3%
M 15
 
4.0%
13
 
3.4%
Other values (19) 106
28.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 361
95.5%
Space Separator 13
 
3.4%
Decimal Number 4
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 44
12.2%
R 35
 
9.7%
E 32
 
8.9%
O 29
 
8.0%
S 29
 
8.0%
I 26
 
7.2%
T 25
 
6.9%
N 24
 
6.6%
M 15
 
4.2%
L 12
 
3.3%
Other values (15) 90
24.9%
Decimal Number
ValueCountFrequency (%)
7 2
50.0%
2 1
25.0%
6 1
25.0%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 361
95.5%
Common 17
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 44
12.2%
R 35
 
9.7%
E 32
 
8.9%
O 29
 
8.0%
S 29
 
8.0%
I 26
 
7.2%
T 25
 
6.9%
N 24
 
6.6%
M 15
 
4.2%
L 12
 
3.3%
Other values (15) 90
24.9%
Common
ValueCountFrequency (%)
13
76.5%
7 2
 
11.8%
2 1
 
5.9%
6 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 44
11.6%
R 35
 
9.3%
E 32
 
8.5%
O 29
 
7.7%
S 29
 
7.7%
I 26
 
6.9%
T 25
 
6.6%
N 24
 
6.3%
M 15
 
4.0%
13
 
3.4%
Other values (19) 106
28.0%

SHIP_KIND
Categorical

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
Inland Motor Tanker liquid cargo type C
18 
Inland Motor Tanker
16 
LNG Tanker
3 
Inland Unknown
2 
Inland Motor Tanker dry cargo as if liquid
2 
Other values (6)
8 

Length

Max length42
Median length39
Mean length25.571429
Min length4

Unique

Unique4 ?
Unique (%)8.2%

Sample

1st rowInland Motor Tanker
2nd rowInland Unknown
3rd rowInland Motor Tanker dry cargo as if liquid
4th rowLNG Tanker
5th rowInland Motor Tanker

Common Values

ValueCountFrequency (%)
Inland Motor Tanker liquid cargo type C 18
36.7%
Inland Motor Tanker 16
32.7%
LNG Tanker 3
 
6.1%
Inland Unknown 2
 
4.1%
Inland Motor Tanker dry cargo as if liquid 2
 
4.1%
<NA> 2
 
4.1%
Inland Tanker 2
 
4.1%
LPG Tanker 1
 
2.0%
Tanker 1
 
2.0%
Crude Oil Tanker 1
 
2.0%

Length

2023-12-10T23:48:01.482115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tanker 45
20.5%
inland 41
18.7%
motor 37
16.9%
liquid 21
9.6%
cargo 21
9.6%
type 19
8.7%
c 18
 
8.2%
lng 3
 
1.4%
if 2
 
0.9%
na 2
 
0.9%
Other values (7) 10
 
4.6%

SHIP_WDTH
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.041633
Minimum0
Maximum48
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:01.598224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.52
Q19.6
median11.4
Q315
95-th percentile43.4
Maximum48
Range48
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation10.225549
Coefficient of variation (CV)0.72823076
Kurtosis4.8128711
Mean14.041633
Median Absolute Deviation (MAD)1.9
Skewness2.2387884
Sum688.04
Variance104.56185
MonotonicityNot monotonic
2023-12-10T23:48:01.717467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
15.0 5
 
10.2%
9.6 4
 
8.2%
11.5 4
 
8.2%
11.4 4
 
8.2%
11.0 3
 
6.1%
43.4 3
 
6.1%
13.5 2
 
4.1%
8.2 2
 
4.1%
17.5 2
 
4.1%
9.5 2
 
4.1%
Other values (15) 18
36.7%
ValueCountFrequency (%)
0.0 2
4.1%
6.4 1
 
2.0%
6.7 1
 
2.0%
7.1 1
 
2.0%
7.2 1
 
2.0%
7.8 1
 
2.0%
8.2 2
4.1%
9.5 2
4.1%
9.6 4
8.2%
10.2 2
4.1%
ValueCountFrequency (%)
48.0 1
 
2.0%
43.4 3
6.1%
29.2 1
 
2.0%
20.3 1
 
2.0%
17.5 2
 
4.1%
15.0 5
10.2%
13.5 2
 
4.1%
13.0 1
 
2.0%
12.0 2
 
4.1%
11.5 4
8.2%

SHIP_LNTH
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.38571
Minimum0
Maximum280
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:01.840598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.94
Q185
median110
Q3130
95-th percentile265.2
Maximum280
Range280
Interquartile range (IQR)45

Descriptive statistics

Standard deviation59.889374
Coefficient of variation (CV)0.53767554
Kurtosis2.6211605
Mean111.38571
Median Absolute Deviation (MAD)25
Skewness1.2650796
Sum5457.9
Variance3586.7371
MonotonicityNot monotonic
2023-12-10T23:48:01.960666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
110.0 9
18.4%
135.0 7
14.3%
86.0 5
10.2%
125.0 3
 
6.1%
85.0 3
 
6.1%
130.0 2
 
4.1%
109.9 2
 
4.1%
280.0 2
 
4.1%
0.0 2
 
4.1%
55.0 2
 
4.1%
Other values (12) 12
24.5%
ValueCountFrequency (%)
0.0 2
4.1%
34.9 1
2.0%
40.0 1
2.0%
46.0 1
2.0%
55.0 2
4.1%
60.0 1
2.0%
66.0 1
2.0%
80.0 1
2.0%
81.2 1
2.0%
82.0 1
2.0%
ValueCountFrequency (%)
280.0 2
 
4.1%
266.0 1
 
2.0%
264.0 1
 
2.0%
172.0 1
 
2.0%
135.0 7
14.3%
130.0 2
 
4.1%
125.0 3
 
6.1%
121.0 1
 
2.0%
110.0 9
18.4%
109.9 2
 
4.1%

SHIP_HGHT
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
44 
26.0
 
3
18.2
 
1
23.2
 
1

Length

Max length4
Median length3
Mean length3.1020408
Min length3

Unique

Unique2 ?
Unique (%)4.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row26.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 44
89.8%
26.0 3
 
6.1%
18.2 1
 
2.0%
23.2 1
 
2.0%

Length

2023-12-10T23:48:02.098787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:02.211633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44
89.8%
26.0 3
 
6.1%
18.2 1
 
2.0%
23.2 1
 
2.0%

SHIP_OWNER_NM
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing48
Missing (%)98.0%
Memory size524.0 B
2023-12-10T23:48:02.604427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowEURONAV
ValueCountFrequency (%)
euronav 1
100.0%
2023-12-10T23:48:02.821482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1
14.3%
U 1
14.3%
R 1
14.3%
O 1
14.3%
N 1
14.3%
A 1
14.3%
V 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1
14.3%
U 1
14.3%
R 1
14.3%
O 1
14.3%
N 1
14.3%
A 1
14.3%
V 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 7
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1
14.3%
U 1
14.3%
R 1
14.3%
O 1
14.3%
N 1
14.3%
A 1
14.3%
V 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1
14.3%
U 1
14.3%
R 1
14.3%
O 1
14.3%
N 1
14.3%
A 1
14.3%
V 1
14.3%

DRAFT
Categorical

IMBALANCE 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
45 
11.6
 
1
10.6
 
1
11.4
 
1
17.0
 
1

Length

Max length4
Median length3
Mean length3.0816327
Min length3

Unique

Unique4 ?
Unique (%)8.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row11.6
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45
91.8%
11.6 1
 
2.0%
10.6 1
 
2.0%
11.4 1
 
2.0%
17.0 1
 
2.0%

Length

2023-12-10T23:48:02.941653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:03.060784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45
91.8%
11.6 1
 
2.0%
10.6 1
 
2.0%
11.4 1
 
2.0%
17.0 1
 
2.0%

SHPYRD_NM
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
<NA>
42 
DSME
 
4
HYUNDAI HEAVY INDUSTRIES
 
2
"SAMSUNG SHIPBUILDING & HEAVY INDUSTRIES - GEOJE, KR"
 
1

Length

Max length53
Median length4
Mean length5.8163265
Min length4

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th rowDSME
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 42
85.7%
DSME 4
 
8.2%
HYUNDAI HEAVY INDUSTRIES 2
 
4.1%
"SAMSUNG SHIPBUILDING & HEAVY INDUSTRIES - GEOJE, KR" 1
 
2.0%

Length

2023-12-10T23:48:03.193692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:03.316875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 42
70.0%
dsme 4
 
6.7%
heavy 3
 
5.0%
industries 3
 
5.0%
hyundai 2
 
3.3%
2
 
3.3%
samsung 1
 
1.7%
shipbuilding 1
 
1.7%
geoje 1
 
1.7%
kr 1
 
1.7%

BULD_YR
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1061.7347
Minimum0
Maximum2013
Zeros23
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:03.432027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1900
Q32010
95-th percentile2012
Maximum2013
Range2013
Interquartile range (IQR)2010

Descriptive statistics

Standard deviation1009.1823
Coefficient of variation (CV)0.95050323
Kurtosis-2.068763
Mean1061.7347
Median Absolute Deviation (MAD)113
Skewness-0.12521844
Sum52025
Variance1018448.8
MonotonicityNot monotonic
2023-12-10T23:48:03.552299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 23
46.9%
2010 4
 
8.2%
2011 4
 
8.2%
2012 4
 
8.2%
2009 3
 
6.1%
2006 2
 
4.1%
1900 2
 
4.1%
2008 2
 
4.1%
2013 2
 
4.1%
2005 1
 
2.0%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
0 23
46.9%
1900 2
 
4.1%
2003 1
 
2.0%
2004 1
 
2.0%
2005 1
 
2.0%
2006 2
 
4.1%
2008 2
 
4.1%
2009 3
 
6.1%
2010 4
 
8.2%
2011 4
 
8.2%
ValueCountFrequency (%)
2013 2
4.1%
2012 4
8.2%
2011 4
8.2%
2010 4
8.2%
2009 3
6.1%
2008 2
4.1%
2006 2
4.1%
2005 1
 
2.0%
2004 1
 
2.0%
2003 1
 
2.0%

DDWGHT
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9930.6327
Minimum0
Maximum158765
Zeros28
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:03.696319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32999
95-th percentile80405.2
Maximum158765
Range158765
Interquartile range (IQR)2999

Descriptive statistics

Standard deviation29291.99
Coefficient of variation (CV)2.94966
Kurtosis15.025936
Mean9930.6327
Median Absolute Deviation (MAD)0
Skewness3.7611779
Sum486601
Variance8.5802069 × 108
MonotonicityNot monotonic
2023-12-10T23:48:03.826878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 28
57.1%
1700 2
 
4.1%
1600 2
 
4.1%
29328 1
 
2.0%
3463 1
 
2.0%
83200 1
 
2.0%
2999 1
 
2.0%
6350 1
 
2.0%
4250 1
 
2.0%
158765 1
 
2.0%
Other values (10) 10
 
20.4%
ValueCountFrequency (%)
0 28
57.1%
1600 2
 
4.1%
1650 1
 
2.0%
1700 2
 
4.1%
2500 1
 
2.0%
2860 1
 
2.0%
2898 1
 
2.0%
2999 1
 
2.0%
3200 1
 
2.0%
3463 1
 
2.0%
ValueCountFrequency (%)
158765 1
2.0%
83200 1
2.0%
82500 1
2.0%
77263 1
2.0%
29328 1
2.0%
8556 1
2.0%
6350 1
2.0%
5900 1
2.0%
4319 1
2.0%
4250 1
2.0%

DPTR_HMS
Categorical

IMBALANCE 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2021 00:00:00
41 
02-Jan-2021 00:00:00
 
4
03-Jan-2021 00:00:00
 
2
07-Jan-2021 00:00:00
 
1
05-Jan-2021 00:00:00
 
1

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique2 ?
Unique (%)4.1%

Sample

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

Common Values

ValueCountFrequency (%)
01-Jan-2021 00:00:00 41
83.7%
02-Jan-2021 00:00:00 4
 
8.2%
03-Jan-2021 00:00:00 2
 
4.1%
07-Jan-2021 00:00:00 1
 
2.0%
05-Jan-2021 00:00:00 1
 
2.0%

Length

2023-12-10T23:48:03.964980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:04.077737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 49
50.0%
01-jan-2021 41
41.8%
02-jan-2021 4
 
4.1%
03-jan-2021 2
 
2.0%
07-jan-2021 1
 
1.0%
05-jan-2021 1
 
1.0%

ARVL_HMS
Categorical

IMBALANCE 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
13-Oct-2021 18:00:00
36 
12-Oct-2021 00:00:00
 
2
13-Oct-2021 12:00:00
 
2
16-Jun-2021 06:00:00
 
1
07-Oct-2021 00:00:00
 
1
Other values (7)
7 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique9 ?
Unique (%)18.4%

Sample

1st row13-Oct-2021 18:00:00
2nd row13-Oct-2021 18:00:00
3rd row13-Oct-2021 18:00:00
4th row12-Oct-2021 00:00:00
5th row13-Oct-2021 18:00:00

Common Values

ValueCountFrequency (%)
13-Oct-2021 18:00:00 36
73.5%
12-Oct-2021 00:00:00 2
 
4.1%
13-Oct-2021 12:00:00 2
 
4.1%
16-Jun-2021 06:00:00 1
 
2.0%
07-Oct-2021 00:00:00 1
 
2.0%
23-Apr-2021 06:00:00 1
 
2.0%
11-Oct-2021 18:00:00 1
 
2.0%
12-Oct-2021 18:00:00 1
 
2.0%
06-May-2021 12:00:00 1
 
2.0%
12-Oct-2021 06:00:00 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2023-12-10T23:48:04.186938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13-oct-2021 39
39.8%
18:00:00 38
38.8%
06:00:00 5
 
5.1%
12-oct-2021 4
 
4.1%
00:00:00 3
 
3.1%
12:00:00 3
 
3.1%
16-jun-2021 1
 
1.0%
07-oct-2021 1
 
1.0%
23-apr-2021 1
 
1.0%
11-oct-2021 1
 
1.0%
Other values (2) 2
 
2.0%

WAVE_AVE_VE_1M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.33743
Minimum0.0249719
Maximum9.0282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:04.330387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0249719
5-th percentile0.2969976
Q10.608768
median1.06648
Q31.40668
95-th percentile2.56709
Maximum9.0282
Range9.0032281
Interquartile range (IQR)0.797912

Descriptive statistics

Standard deviation1.5093604
Coefficient of variation (CV)1.1285528
Kurtosis17.486477
Mean1.33743
Median Absolute Deviation (MAD)0.443687
Skewness3.9739406
Sum65.534071
Variance2.2781689
MonotonicityNot monotonic
2023-12-10T23:48:04.477136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.436009 1
 
2.0%
0.752157 1
 
2.0%
0.608768 1
 
2.0%
1.06648 1
 
2.0%
0.528122 1
 
2.0%
1.2504 1
 
2.0%
1.90304 1
 
2.0%
1.44726 1
 
2.0%
1.2495 1
 
2.0%
0.577964 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0249719 1
2.0%
0.103847 1
2.0%
0.277054 1
2.0%
0.326913 1
2.0%
0.436009 1
2.0%
0.444834 1
2.0%
0.476958 1
2.0%
0.484166 1
2.0%
0.528122 1
2.0%
0.561258 1
2.0%
ValueCountFrequency (%)
9.0282 1
2.0%
7.00061 1
2.0%
2.97641 1
2.0%
1.95311 1
2.0%
1.91469 1
2.0%
1.90304 1
2.0%
1.77032 1
2.0%
1.66372 1
2.0%
1.55564 1
2.0%
1.54476 1
2.0%

WAVE_AVE_VE_2M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5443645
Minimum0.00659562
Maximum10.0986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:04.635694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00659562
5-th percentile0.09472726
Q10.558503
median1.00256
Q31.48888
95-th percentile6.542058
Maximum10.0986
Range10.092004
Interquartile range (IQR)0.930377

Descriptive statistics

Standard deviation2.150092
Coefficient of variation (CV)1.3922179
Kurtosis10.657532
Mean1.5443645
Median Absolute Deviation (MAD)0.469355
Skewness3.325164
Sum75.673861
Variance4.6228954
MonotonicityNot monotonic
2023-12-10T23:48:04.794545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.310439 1
 
2.0%
0.508492 1
 
2.0%
0.0444501 1
 
2.0%
1.28235 1
 
2.0%
0.48376 1
 
2.0%
1.10153 1
 
2.0%
2.23279 1
 
2.0%
1.33805 1
 
2.0%
1.48888 1
 
2.0%
0.799477 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.00659562 1
2.0%
0.0299131 1
2.0%
0.0444501 1
2.0%
0.170143 1
2.0%
0.29879 1
2.0%
0.2991 1
2.0%
0.310439 1
2.0%
0.48376 1
2.0%
0.508492 1
2.0%
0.518337 1
2.0%
ValueCountFrequency (%)
10.0986 1
2.0%
9.50735 1
2.0%
9.02207 1
2.0%
2.82204 1
2.0%
2.23279 1
2.0%
2.0749 1
2.0%
2.06858 1
2.0%
1.71225 1
2.0%
1.60849 1
2.0%
1.55715 1
2.0%

WAVE_AVE_VE_3M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6152896
Minimum9.68182 × 10-5
Maximum25.6926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:04.939236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.68182 × 10-5
5-th percentile0.10667744
Q10.43076
median0.803075
Q31.58241
95-th percentile2.956558
Maximum25.6926
Range25.692503
Interquartile range (IQR)1.15165

Descriptive statistics

Standard deviation3.701538
Coefficient of variation (CV)2.291563
Kurtosis39.233989
Mean1.6152896
Median Absolute Deviation (MAD)0.510404
Skewness6.0500944
Sum79.149192
Variance13.701384
MonotonicityNot monotonic
2023-12-10T23:48:05.097533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.341124 1
 
2.0%
0.715136 1
 
2.0%
0.00750812 1
 
2.0%
0.876687 1
 
2.0%
0.421897 1
 
2.0%
0.523195 1
 
2.0%
1.63247 1
 
2.0%
1.44836 1
 
2.0%
1.20091 1
 
2.0%
0.487837 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9.68182e-05 1
2.0%
0.00750812 1
2.0%
0.0538404 1
2.0%
0.185933 1
2.0%
0.250878 1
2.0%
0.291954 1
2.0%
0.292671 1
2.0%
0.305248 1
2.0%
0.322722 1
2.0%
0.341124 1
2.0%
ValueCountFrequency (%)
25.6926 1
2.0%
7.57104 1
2.0%
3.22783 1
2.0%
2.54965 1
2.0%
2.0897 1
2.0%
1.88022 1
2.0%
1.86702 1
2.0%
1.81423 1
2.0%
1.78531 1
2.0%
1.7268 1
2.0%

WAVE_AVE_VE_4M
Categorical

IMBALANCE 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
45 
5.19771e-05
 
1
0.00466486
 
1
0.000960057
 
1
9.67028
 
1

Length

Max length11
Median length3
Mean length3.5510204
Min length3

Unique

Unique4 ?
Unique (%)8.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row5.19771e-05
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45
91.8%
5.19771e-05 1
 
2.0%
0.00466486 1
 
2.0%
0.000960057 1
 
2.0%
9.67028 1
 
2.0%

Length

2023-12-10T23:48:05.255337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:05.407321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45
91.8%
5.19771e-05 1
 
2.0%
0.00466486 1
 
2.0%
0.000960057 1
 
2.0%
9.67028 1
 
2.0%

WAVE_AVE_VE_5M
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
10.7533
 
1

Length

Max length7
Median length3
Mean length3.0816327
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
10.7533 1
 
2.0%

Length

2023-12-10T23:48:05.519737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:05.614138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
10.7533 1
 
2.0%

WAVE_AVE_VE_6M
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
11.1999
 
1

Length

Max length7
Median length3
Mean length3.0816327
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
11.1999 1
 
2.0%

Length

2023-12-10T23:48:05.715922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:05.826206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
11.1999 1
 
2.0%

WAVE_AVE_VE_7M
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
9.51989
 
1

Length

Max length7
Median length3
Mean length3.0816327
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
9.51989 1
 
2.0%

Length

2023-12-10T23:48:05.942694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:06.038376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
9.51989 1
 
2.0%

WAVE_AVE_VE_8M
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
10.8571
 
1

Length

Max length7
Median length3
Mean length3.0816327
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
10.8571 1
 
2.0%

Length

2023-12-10T23:48:06.143067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:06.250637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
10.8571 1
 
2.0%

WAVE_AVE_VE_9M
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

2023-12-10T23:48:06.333697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:06.415984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

WAVE_AVE_VE_10M_ABOVE
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
9.48389
 
1

Length

Max length7
Median length3
Mean length3.0816327
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
9.48389 1
 
2.0%

Length

2023-12-10T23:48:06.506910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:06.603341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
9.48389 1
 
2.0%

WAVE_MAX_VE_1M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.319344
Minimum1.64185
Maximum29.0821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:06.694244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.64185
5-th percentile3.6314
Q17.59476
median8.54211
Q39.98894
95-th percentile28.22238
Maximum29.0821
Range27.44025
Interquartile range (IQR)2.39418

Descriptive statistics

Standard deviation6.3449862
Coefficient of variation (CV)0.61486335
Kurtosis3.7487632
Mean10.319344
Median Absolute Deviation (MAD)1.11757
Skewness2.0038938
Sum505.64784
Variance40.25885
MonotonicityNot monotonic
2023-12-10T23:48:06.818273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8.68502 1
 
2.0%
8.65656 1
 
2.0%
28.4019 1
 
2.0%
8.39301 1
 
2.0%
7.51957 1
 
2.0%
9.59876 1
 
2.0%
9.67502 1
 
2.0%
9.79418 1
 
2.0%
8.37626 1
 
2.0%
8.83843 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1.64185 1
2.0%
1.98659 1
2.0%
3.38368 1
2.0%
4.00298 1
2.0%
5.10913 1
2.0%
6.28037 1
2.0%
6.97816 1
2.0%
7.09578 1
2.0%
7.23161 1
2.0%
7.42454 1
2.0%
ValueCountFrequency (%)
29.0821 1
2.0%
29.0114 1
2.0%
28.4019 1
2.0%
27.9531 1
2.0%
19.3652 1
2.0%
17.9192 1
2.0%
15.0707 1
2.0%
14.9267 1
2.0%
10.7899 1
2.0%
10.2524 1
2.0%

WAVE_MAX_VE_2M
Real number (ℝ)

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2205892
Minimum0.1
Maximum28.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:06.949763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.436886
Q16.64532
median7.85475
Q38.57021
95-th percentile23.7101
Maximum28.37
Range28.27
Interquartile range (IQR)1.92489

Descriptive statistics

Standard deviation6.4719471
Coefficient of variation (CV)0.70190169
Kurtosis2.0791041
Mean9.2205892
Median Absolute Deviation (MAD)1.20538
Skewness1.5489416
Sum451.80887
Variance41.8861
MonotonicityNot monotonic
2023-12-10T23:48:07.079889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.1 2
 
4.1%
4.66807 1
 
2.0%
7.97759 1
 
2.0%
8.32084 1
 
2.0%
6.64532 1
 
2.0%
7.08314 1
 
2.0%
10.1807 1
 
2.0%
7.57422 1
 
2.0%
7.78324 1
 
2.0%
23.3948 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
0.1 2
4.1%
1.43085 1
2.0%
1.44594 1
2.0%
1.62152 1
2.0%
3.22684 1
2.0%
4.66807 1
2.0%
5.69951 1
2.0%
5.7626 1
2.0%
6.28975 1
2.0%
6.48214 1
2.0%
ValueCountFrequency (%)
28.37 1
2.0%
26.6236 1
2.0%
23.9203 1
2.0%
23.3948 1
2.0%
21.8051 1
2.0%
21.1977 1
2.0%
17.6992 1
2.0%
14.886 1
2.0%
10.1807 1
2.0%
10.0713 1
2.0%

WAVE_MAX_VE_3M
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1258586
Minimum9.68182 × 10-5
Maximum25.6926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:07.315902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.68182 × 10-5
5-th percentile0.6620962
Q13.74076
median6.38975
Q37.2675
95-th percentile12.23708
Maximum25.6926
Range25.692503
Interquartile range (IQR)3.52674

Descriptive statistics

Standard deviation4.1400129
Coefficient of variation (CV)0.67582575
Kurtosis9.876002
Mean6.1258586
Median Absolute Deviation (MAD)1.18241
Skewness2.2953797
Sum300.16707
Variance17.139707
MonotonicityNot monotonic
2023-12-10T23:48:07.499458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5.12182 1
 
2.0%
6.39093 1
 
2.0%
0.00750812 1
 
2.0%
7.45735 1
 
2.0%
5.48806 1
 
2.0%
5.69097 1
 
2.0%
9.82115 1
 
2.0%
7.64087 1
 
2.0%
4.40956 1
 
2.0%
2.0607 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9.68182e-05 1
2.0%
0.00750812 1
2.0%
0.609091 1
2.0%
0.741604 1
2.0%
1.63483 1
2.0%
1.72252 1
2.0%
2.0607 1
2.0%
2.38643 1
2.0%
2.46423 1
2.0%
3.02571 1
2.0%
ValueCountFrequency (%)
25.6926 1
2.0%
14.9195 1
2.0%
13.8477 1
2.0%
9.82115 1
2.0%
8.64388 1
2.0%
7.9587 1
2.0%
7.64435 1
2.0%
7.64087 1
2.0%
7.57216 1
2.0%
7.56054 1
2.0%

WAVE_MAX_VE_4M
Categorical

IMBALANCE 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
45 
5.19771e-05
 
1
0.0084476
 
1
0.00156234
 
1
14.5789
 
1

Length

Max length11
Median length3
Mean length3.5102041
Min length3

Unique

Unique4 ?
Unique (%)8.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row5.19771e-05
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45
91.8%
5.19771e-05 1
 
2.0%
0.0084476 1
 
2.0%
0.00156234 1
 
2.0%
14.5789 1
 
2.0%

Length

2023-12-10T23:48:07.640738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:07.756702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45
91.8%
5.19771e-05 1
 
2.0%
0.0084476 1
 
2.0%
0.00156234 1
 
2.0%
14.5789 1
 
2.0%

WAVE_MAX_VE_5M
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
13.69
 
1

Length

Max length5
Median length3
Mean length3.0408163
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
13.69 1
 
2.0%

Length

2023-12-10T23:48:07.885151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:08.001713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
13.69 1
 
2.0%

WAVE_MAX_VE_6M
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
12.885
 
1

Length

Max length6
Median length3
Mean length3.0612245
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
12.885 1
 
2.0%

Length

2023-12-10T23:48:08.115149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:08.211308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
12.885 1
 
2.0%

WAVE_MAX_VE_7M
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
12.8133
 
1

Length

Max length7
Median length3
Mean length3.0816327
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
12.8133 1
 
2.0%

Length

2023-12-10T23:48:08.331856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:08.438607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
12.8133 1
 
2.0%

WAVE_MAX_VE_8M
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
13.7661
 
1

Length

Max length7
Median length3
Mean length3.0816327
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
13.7661 1
 
2.0%

Length

2023-12-10T23:48:08.556416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:09.068920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
13.7661 1
 
2.0%

WAVE_MAX_VE_9M
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

2023-12-10T23:48:09.166750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:09.270727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

WAVE_MAX_VE_10M_ABOVE
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
9.48389
 
1

Length

Max length7
Median length3
Mean length3.0816327
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
9.48389 1
 
2.0%

Length

2023-12-10T23:48:09.394469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:09.506411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
9.48389 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
2023-12-10T23:48:09.635259image/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
2023-12-10T23:48:09.791408image/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_HMSWAVE_AVE_VE_1MWAVE_AVE_VE_2MWAVE_AVE_VE_3MWAVE_AVE_VE_4MWAVE_AVE_VE_5MWAVE_AVE_VE_6MWAVE_AVE_VE_7MWAVE_AVE_VE_8MWAVE_AVE_VE_9MWAVE_AVE_VE_10M_ABOVEWAVE_MAX_VE_1MWAVE_MAX_VE_2MWAVE_MAX_VE_3MWAVE_MAX_VE_4MWAVE_MAX_VE_5MWAVE_MAX_VE_6MWAVE_MAX_VE_7MWAVE_MAX_VE_8MWAVE_MAX_VE_9MWAVE_MAX_VE_10M_ABOVERN
02054359900VERANOInland Motor Tanker11.0125.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:000.4360090.3104390.3411240.00.00.00.00.000.08.685024.668075.121820.00.00.00.00.000.02
12054361900PORRENAInland Unknown7.255.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:000.1038470.1701430.1859330.00.00.00.00.000.04.002983.226841.722520.00.00.00.00.000.03
22054422900TARSISInland Motor Tanker dry cargo as if liquid10.485.00.0<NA>0.0<NA>0002-Jan-2021 00:00:0013-Oct-2021 18:00:002.976412.822043.227830.00.00.00.00.000.019.36528.474888.643880.00.00.00.00.000.04
32054450009322255SUMMIT LNGLNG Tanker43.4266.026.0<NA>11.6DSME20067726302-Jan-2021 00:00:0012-Oct-2021 00:00:000.3269130.0065960.0000970.0000520.00.00.00.000.027.95310.10.0000970.0000520.00.00.00.000.05
42054465900MTS VIVALDIInland Motor Tanker9.685.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:001.09481.002561.582410.00.00.00.00.000.08.542116.571267.644350.00.00.00.00.000.06
52054488900DEUGNIETInland Motor Tanker6.734.90.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:000.6227930.8624160.6253220.00.00.00.00.000.03.3836821.19773.025710.00.00.00.00.000.07
62054526900SINJOORInland Motor Tanker9.686.00.0<NA>0.0<NA>2009001-Jan-2021 00:00:0013-Oct-2021 18:00:000.6007190.5523780.430760.00.00.00.00.000.07.592975.699514.080650.00.00.00.00.000.08
72054536900BIRJO 2Inland Motor Tanker11.4125.00.0<NA>0.0<NA>2005346307-Jan-2021 00:00:0013-Oct-2021 18:00:001.230391.350450.6211390.00.00.00.00.000.010.01410.07133.740760.00.00.00.00.000.09
82054575900THALYSInland Motor Tanker liquid cargo type C17.5135.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:001.406681.420580.8030750.00.00.00.00.000.08.185717.854755.773580.00.00.00.00.000.010
92054600009292761LIBRAMONTLPG Tanker29.2172.018.2<NA>10.6DSME20062932801-Jan-2021 00:00:0013-Oct-2021 18:00:009.02829.507352.549650.00.00.00.00.000.017.919217.699214.91950.00.00.00.00.000.011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSWAVE_AVE_VE_1MWAVE_AVE_VE_2MWAVE_AVE_VE_3MWAVE_AVE_VE_4MWAVE_AVE_VE_5MWAVE_AVE_VE_6MWAVE_AVE_VE_7MWAVE_AVE_VE_8MWAVE_AVE_VE_9MWAVE_AVE_VE_10M_ABOVEWAVE_MAX_VE_1MWAVE_MAX_VE_2MWAVE_MAX_VE_3MWAVE_MAX_VE_4MWAVE_MAX_VE_5MWAVE_MAX_VE_6MWAVE_MAX_VE_7MWAVE_MAX_VE_8MWAVE_MAX_VE_9MWAVE_MAX_VE_10M_ABOVERN
392055229900THALASSAInland Motor Tanker liquid cargo type C11.4110.00.0<NA>0.0<NA>0002-Jan-2021 00:00:0012-Oct-2021 06:00:001.294951.712251.674160.00.00.00.00.000.07.594768.713176.583410.00.00.00.00.000.041
402055232900HYDRUSInland Motor Tanker13.0110.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:000.8754390.5183371.319840.00.00.00.00.000.08.365698.088966.870840.00.00.00.00.000.042
412055238900TRAFUCO 7Inland Motor Tanker8.260.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:000.4448340.5332050.2508780.00.00.00.00.000.07.095786.289752.386430.00.00.00.00.000.043
422055242900CAYMANInland Motor Tanker liquid cargo type C11.4109.90.0<NA>0.0<NA>2004299901-Jan-2021 00:00:0013-Oct-2021 18:00:001.914692.07491.356660.00.00.00.00.000.014.92677.948646.389750.00.00.00.00.000.044
432055243900SOMTRANS XXVIIIInland Tanker15.0135.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:001.263771.511561.814230.00.00.00.00.000.08.192997.45297.397320.00.00.00.00.000.045
442055253900ANTARESInland Motor Tanker liquid cargo type C15.0135.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:000.9352891.062811.18340.00.00.00.00.000.08.597598.55947.560540.00.00.00.00.000.046
452055260009361445EXPRESSLNG Tanker43.4280.026.0<NA>0.0DSME20098320001-Jan-2021 00:00:0013-Oct-2021 06:00:001.5107110.098625.69260.00.00.00.00.000.029.082128.3725.69260.00.00.00.00.000.047
462055262900SOMTRANS XXIXInland Motor Tanker liquid cargo type C15.0135.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:001.266930.956412.08970.00.00.00.00.000.07.820097.897386.631380.00.00.00.00.000.048
472055263900STRAUSSInland Motor Tanker liquid cargo type C20.3109.90.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:001.175571.203250.6458970.00.00.00.00.000.07.424546.785815.49590.00.00.00.00.000.049
482055268909367918<NA><NA>0.00.00.0<NA>0.0HYUNDAI HEAVY INDUSTRIES1900001-Jan-2021 00:00:0024-Mar-2021 06:00:001.663721.435321.785310.00.00.00.00.000.08.067117.279686.286980.00.00.00.00.000.050