Overview

Dataset statistics

Number of variables24
Number of observations49
Missing cells90
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory212.7 B

Variable types

Numeric15
Text3
Categorical6

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000451

Alerts

SHIP_OWNER_NM has constant value ""Constant
DPTR_HMS is highly imbalanced (63.2%)Imbalance
RL_POWER is highly imbalanced (75.4%)Imbalance
FUEL_CNSMP_QTY is highly imbalanced (75.4%)Imbalance
CDBX is highly imbalanced (75.4%)Imbalance
SHIP_NM has 1 (2.0%) missing valuesMissing
SHIP_OWNER_NM has 48 (98.0%) missing valuesMissing
SHPYRD_NM has 41 (83.7%) missing valuesMissing
MMSI 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
RN has unique valuesUnique
IMO_IDNTF_NO has 38 (77.6%) zerosZeros
SHIP_WDTH has 1 (2.0%) zerosZeros
SHIP_LNTH has 1 (2.0%) zerosZeros
SHIP_HGHT has 42 (85.7%) zerosZeros
DRAFT has 43 (87.8%) zerosZeros
BULD_YR has 22 (44.9%) zerosZeros
DDWGHT has 25 (51.0%) zerosZeros
ADDTI_RSTC has 1 (2.0%) zerosZeros
TOT_RSTC has 1 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:44:25.287240
Analysis finished2023-12-10 14:44:25.553108
Duration0.27 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%
Mean2.0664046 × 108
Minimum2.0546569 × 108
Maximum2.111419 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:25.621029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0546569 × 108
5-th percentile2.0548401 × 108
Q12.0550279 × 108
median2.0551569 × 108
Q32.0552629 × 108
95-th percentile2.1112726 × 108
Maximum2.111419 × 108
Range5676210
Interquartile range (IQR)23500

Descriptive statistics

Standard deviation2161803.6
Coefficient of variation (CV)0.010461667
Kurtosis0.28463089
Mean2.0664046 × 108
Median Absolute Deviation (MAD)12000
Skewness1.4668693
Sum1.0125383 × 1010
Variance4.6733948 × 1012
MonotonicityStrictly increasing
2023-12-10T23:44:26.018578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
205465690 1
 
2.0%
205526390 1
 
2.0%
205521690 1
 
2.0%
205521790 1
 
2.0%
205522990 1
 
2.0%
205523290 1
 
2.0%
205523890 1
 
2.0%
205524290 1
 
2.0%
205524390 1
 
2.0%
205525390 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
205465690 1
2.0%
205479890 1
2.0%
205481290 1
2.0%
205488090 1
2.0%
205488290 1
2.0%
205490890 1
2.0%
205491290 1
2.0%
205495690 1
2.0%
205498490 1
2.0%
205500990 1
2.0%
ValueCountFrequency (%)
211141900 1
2.0%
211135000 1
2.0%
211129800 1
2.0%
211123456 1
2.0%
211000598 1
2.0%
211000001 1
2.0%
210979000 1
2.0%
210857000 1
2.0%
209249000 1
2.0%
209240000 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3852152.1
Minimum0
Maximum99999999
Zeros38
Zeros (%)77.6%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:26.131096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9373020.8
Maximum99999999
Range99999999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14490662
Coefficient of variation (CV)3.7617055
Kurtosis42.570564
Mean3852152.1
Median Absolute Deviation (MAD)0
Skewness6.3397455
Sum1.8875545 × 108
Variance2.0997928 × 1014
MonotonicityNot monotonic
2023-12-10T23:44:26.237613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 38
77.6%
9361079 1
 
2.0%
9380738 1
 
2.0%
9361445 1
 
2.0%
9301419 1
 
2.0%
9430210 1
 
2.0%
9254006 1
 
2.0%
9277759 1
 
2.0%
9277773 1
 
2.0%
99999999 1
 
2.0%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
0 38
77.6%
4812830 1
 
2.0%
9254006 1
 
2.0%
9277759 1
 
2.0%
9277773 1
 
2.0%
9298193 1
 
2.0%
9301419 1
 
2.0%
9361079 1
 
2.0%
9361445 1
 
2.0%
9380738 1
 
2.0%
ValueCountFrequency (%)
99999999 1
2.0%
9430210 1
2.0%
9380738 1
2.0%
9361445 1
2.0%
9361079 1
2.0%
9301419 1
2.0%
9298193 1
2.0%
9277773 1
2.0%
9277759 1
2.0%
9254006 1
2.0%

SHIP_NM
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Memory size524.0 B
2023-12-10T23:44:26.452079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length8.375
Min length4

Characters and Unicode

Total characters402
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st rowOLIEVINKER IV
2nd rowNEW YORK
3rd rowGUATEMALA
4th rowMAC 7
5th rowROSSINI
ValueCountFrequency (%)
7 2
 
3.2%
somtrans 2
 
3.2%
trafuco 2
 
3.2%
olievinker 1
 
1.6%
senkevich 1
 
1.6%
mantyrano 1
 
1.6%
antibes 1
 
1.6%
thalassa 1
 
1.6%
hydrus 1
 
1.6%
cayman 1
 
1.6%
Other values (50) 50
79.4%
2023-12-10T23:44:26.777180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 48
 
11.9%
E 40
 
10.0%
R 38
 
9.5%
S 29
 
7.2%
N 29
 
7.2%
O 27
 
6.7%
I 22
 
5.5%
T 21
 
5.2%
15
 
3.7%
M 13
 
3.2%
Other values (19) 120
29.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 383
95.3%
Space Separator 15
 
3.7%
Decimal Number 3
 
0.7%
Other Punctuation 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 48
12.5%
E 40
 
10.4%
R 38
 
9.9%
S 29
 
7.6%
N 29
 
7.6%
O 27
 
7.0%
I 22
 
5.7%
T 21
 
5.5%
M 13
 
3.4%
U 12
 
3.1%
Other values (15) 104
27.2%
Decimal Number
ValueCountFrequency (%)
7 2
66.7%
6 1
33.3%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 383
95.3%
Common 19
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 48
12.5%
E 40
 
10.4%
R 38
 
9.9%
S 29
 
7.6%
N 29
 
7.6%
O 27
 
7.0%
I 22
 
5.7%
T 21
 
5.5%
M 13
 
3.4%
U 12
 
3.1%
Other values (15) 104
27.2%
Common
ValueCountFrequency (%)
15
78.9%
7 2
 
10.5%
6 1
 
5.3%
. 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 402
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 48
 
11.9%
E 40
 
10.0%
R 38
 
9.5%
S 29
 
7.2%
N 29
 
7.2%
O 27
 
6.7%
I 22
 
5.5%
T 21
 
5.2%
15
 
3.7%
M 13
 
3.2%
Other values (19) 120
29.9%

SHIP_KIND
Categorical

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
Inland Motor Tanker liquid cargo type C
18 
Inland Motor Tanker
13 
Crude Oil Tanker
3 
Inland Tanker
2 
LNG Tanker
2 
Other values (10)
11 

Length

Max length42
Median length39
Mean length25.857143
Min length4

Unique

Unique9 ?
Unique (%)18.4%

Sample

1st rowInland Motor Tanker liquid cargo type C
2nd rowTanker
3rd row<NA>
4th rowInland Motor Tanker liquid cargo type C
5th rowInland Motor Tanker

Common Values

ValueCountFrequency (%)
Inland Motor Tanker liquid cargo type C 18
36.7%
Inland Motor Tanker 13
26.5%
Crude Oil Tanker 3
 
6.1%
Inland Tanker 2
 
4.1%
LNG Tanker 2
 
4.1%
Oil or Chemical Tanker 2
 
4.1%
Tanker 1
 
2.0%
<NA> 1
 
2.0%
Inland Unknown 1
 
2.0%
Inland Motor Tanker dry cargo as if liquid 1
 
2.0%
Other values (5) 5
 
10.2%

Length

2023-12-10T23:44:26.902004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tanker 46
20.7%
inland 37
16.7%
motor 33
14.9%
liquid 20
9.0%
cargo 20
9.0%
type 19
8.6%
c 19
8.6%
oil 7
 
3.2%
crude 3
 
1.4%
products 2
 
0.9%
Other values (13) 16
 
7.2%

SHIP_WDTH
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.81102
Minimum0
Maximum48
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:27.004592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.68
Q110.2
median11.5
Q315
95-th percentile42.84
Maximum48
Range48
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation10.716414
Coefficient of variation (CV)0.67778132
Kurtosis2.2534171
Mean15.81102
Median Absolute Deviation (MAD)2.5
Skewness1.6823287
Sum774.74
Variance114.84153
MonotonicityNot monotonic
2023-12-10T23:44:27.131631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
11.5 6
 
12.2%
15.0 5
 
10.2%
11.4 3
 
6.1%
13.5 2
 
4.1%
9.5 2
 
4.1%
9.6 2
 
4.1%
12.0 2
 
4.1%
43.4 2
 
4.1%
32.2 2
 
4.1%
11.0 2
 
4.1%
Other values (19) 21
42.9%
ValueCountFrequency (%)
0.0 1
2.0%
2.5 1
2.0%
6.4 1
2.0%
7.1 1
2.0%
7.8 1
2.0%
8.2 2
4.1%
9.5 2
4.1%
9.6 2
4.1%
10.2 2
4.1%
10.5 1
2.0%
ValueCountFrequency (%)
48.0 1
2.0%
43.4 2
4.1%
42.0 1
2.0%
32.2 2
4.1%
30.0 1
2.0%
28.0 1
2.0%
22.8 1
2.0%
20.3 1
2.0%
17.5 1
2.0%
16.0 1
2.0%

SHIP_LNTH
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.61755
Minimum0
Maximum310
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:27.251875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42.4
Q186
median110
Q3135
95-th percentile273.6
Maximum310
Range310
Interquartile range (IQR)49

Descriptive statistics

Standard deviation65.740841
Coefficient of variation (CV)0.54055389
Kurtosis1.5997838
Mean121.61755
Median Absolute Deviation (MAD)25
Skewness1.1816803
Sum5959.26
Variance4321.8582
MonotonicityNot monotonic
2023-12-10T23:44:27.365848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
110.0 11
22.4%
135.0 6
 
12.2%
86.0 4
 
8.2%
219.0 2
 
4.1%
130.0 2
 
4.1%
280.0 2
 
4.1%
109.9 2
 
4.1%
66.0 1
 
2.0%
60.0 1
 
2.0%
168.0 1
 
2.0%
Other values (17) 17
34.7%
ValueCountFrequency (%)
0.0 1
2.0%
8.5 1
2.0%
40.0 1
2.0%
46.0 1
2.0%
55.0 1
2.0%
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 (%)
310.0 1
 
2.0%
280.0 2
 
4.1%
264.0 1
 
2.0%
234.0 1
 
2.0%
219.0 2
 
4.1%
168.0 1
 
2.0%
135.0 6
12.2%
130.0 2
 
4.1%
125.0 1
 
2.0%
121.0 1
 
2.0%

SHIP_HGHT
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1714286
Minimum0
Maximum26
Zeros42
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:27.465137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22.56
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9317663
Coefficient of variation (CV)2.5010074
Kurtosis3.1002258
Mean3.1714286
Median Absolute Deviation (MAD)0
Skewness2.1946916
Sum155.4
Variance62.912917
MonotonicityNot monotonic
2023-12-10T23:44:27.566370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 42
85.7%
26.0 2
 
4.1%
20.9 2
 
4.1%
23.2 1
 
2.0%
21.6 1
 
2.0%
16.8 1
 
2.0%
ValueCountFrequency (%)
0.0 42
85.7%
16.8 1
 
2.0%
20.9 2
 
4.1%
21.6 1
 
2.0%
23.2 1
 
2.0%
26.0 2
 
4.1%
ValueCountFrequency (%)
26.0 2
 
4.1%
23.2 1
 
2.0%
21.6 1
 
2.0%
20.9 2
 
4.1%
16.8 1
 
2.0%
0.0 42
85.7%

SHIP_OWNER_NM
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing48
Missing (%)98.0%
Memory size524.0 B
2023-12-10T23:44:27.679012image/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%
2023-12-10T23:44:27.883157image/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
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6734694
Minimum0
Maximum17
Zeros43
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:27.987562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.08
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.5814743
Coefficient of variation (CV)2.7377102
Kurtosis4.6208203
Mean1.6734694
Median Absolute Deviation (MAD)0
Skewness2.4899954
Sum82
Variance20.989906
MonotonicityNot monotonic
2023-12-10T23:44:28.076272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 43
87.8%
11.4 1
 
2.0%
17.0 1
 
2.0%
14.5 1
 
2.0%
14.4 1
 
2.0%
13.6 1
 
2.0%
11.1 1
 
2.0%
ValueCountFrequency (%)
0.0 43
87.8%
11.1 1
 
2.0%
11.4 1
 
2.0%
13.6 1
 
2.0%
14.4 1
 
2.0%
14.5 1
 
2.0%
17.0 1
 
2.0%
ValueCountFrequency (%)
17.0 1
 
2.0%
14.5 1
 
2.0%
14.4 1
 
2.0%
13.6 1
 
2.0%
11.4 1
 
2.0%
11.1 1
 
2.0%
0.0 43
87.8%

SHPYRD_NM
Text

MISSING 

Distinct4
Distinct (%)50.0%
Missing41
Missing (%)83.7%
Memory size524.0 B
2023-12-10T23:44:28.212221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length37.5
Mean length21.5
Min length4

Characters and Unicode

Total characters172
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)25.0%

Sample

1st rowHYUNDAI HEAVY INDUSTRIES
2nd rowDSME
3rd rowSAMSUNG SHIPBUILDING & HEAVY INDUSTRIES - GEOJE, KR
4th rowDSME
5th rowHYUNDAI HEAVY INDUSTRIES
ValueCountFrequency (%)
heavy 5
20.8%
industries 5
20.8%
hyundai 4
16.7%
dsme 2
 
8.3%
2
 
8.3%
samsung 1
 
4.2%
shipbuilding 1
 
4.2%
geoje 1
 
4.2%
kr 1
 
4.2%
lindenau 1
 
4.2%
2023-12-10T23:44:28.475019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 19
11.0%
S 16
9.3%
16
9.3%
E 15
8.7%
D 14
8.1%
N 13
 
7.6%
U 12
 
7.0%
A 12
 
7.0%
H 11
 
6.4%
Y 10
 
5.8%
Other values (14) 34
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 153
89.0%
Space Separator 16
 
9.3%
Other Punctuation 2
 
1.2%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 19
12.4%
S 16
10.5%
E 15
9.8%
D 14
9.2%
N 13
8.5%
U 12
7.8%
A 12
7.8%
H 11
7.2%
Y 10
6.5%
R 7
 
4.6%
Other values (10) 24
15.7%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 153
89.0%
Common 19
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 19
12.4%
S 16
10.5%
E 15
9.8%
D 14
9.2%
N 13
8.5%
U 12
7.8%
A 12
7.8%
H 11
7.2%
Y 10
6.5%
R 7
 
4.6%
Other values (10) 24
15.7%
Common
ValueCountFrequency (%)
16
84.2%
& 1
 
5.3%
- 1
 
5.3%
, 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 19
11.0%
S 16
9.3%
16
9.3%
E 15
8.7%
D 14
8.1%
N 13
 
7.6%
U 12
 
7.0%
A 12
 
7.0%
H 11
 
6.4%
Y 10
 
5.8%
Other values (14) 34
19.8%

BULD_YR
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1104.8163
Minimum0
Maximum2018
Zeros22
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:28.586073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2003
Q32010
95-th percentile2012.6
Maximum2018
Range2018
Interquartile range (IQR)2010

Descriptive statistics

Standard deviation1007.7427
Coefficient of variation (CV)0.91213596
Kurtosis-2.0395728
Mean1104.8163
Median Absolute Deviation (MAD)10
Skewness-0.21095808
Sum54136
Variance1015545.4
MonotonicityNot monotonic
2023-12-10T23:44:28.700715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 22
44.9%
2010 4
 
8.2%
2011 4
 
8.2%
2012 4
 
8.2%
2004 4
 
8.2%
2009 2
 
4.1%
2008 2
 
4.1%
2013 2
 
4.1%
1900 1
 
2.0%
2003 1
 
2.0%
Other values (3) 3
 
6.1%
ValueCountFrequency (%)
0 22
44.9%
1900 1
 
2.0%
2002 1
 
2.0%
2003 1
 
2.0%
2004 4
 
8.2%
2005 1
 
2.0%
2008 2
 
4.1%
2009 2
 
4.1%
2010 4
 
8.2%
2011 4
 
8.2%
ValueCountFrequency (%)
2018 1
 
2.0%
2013 2
4.1%
2012 4
8.2%
2011 4
8.2%
2010 4
8.2%
2009 2
4.1%
2008 2
4.1%
2005 1
 
2.0%
2004 4
8.2%
2003 1
 
2.0%

DDWGHT
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13624.204
Minimum0
Maximum158765
Zeros25
Zeros (%)51.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:28.813211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34250
95-th percentile82920
Maximum158765
Range158765
Interquartile range (IQR)4250

Descriptive statistics

Standard deviation33096.89
Coefficient of variation (CV)2.4292715
Kurtosis8.1598343
Mean13624.204
Median Absolute Deviation (MAD)0
Skewness2.8586345
Sum667586
Variance1.0954041 × 109
MonotonicityNot monotonic
2023-12-10T23:44:28.919259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 25
51.0%
74999 2
 
4.1%
1600 2
 
4.1%
1700 2
 
4.1%
4250 1
 
2.0%
32302 1
 
2.0%
3420 1
 
2.0%
4450 1
 
2.0%
100869 1
 
2.0%
83200 1
 
2.0%
Other values (12) 12
24.5%
ValueCountFrequency (%)
0 25
51.0%
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%
3420 1
 
2.0%
ValueCountFrequency (%)
158765 1
2.0%
100869 1
2.0%
83200 1
2.0%
82500 1
2.0%
74999 2
4.1%
32302 1
2.0%
8556 1
2.0%
6350 1
2.0%
5900 1
2.0%
4450 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
42 
02-Jan-2021 00:00:00
 
3
03-Jan-2021 00:00:00
 
2
05-Jan-2021 00:00:00
 
1
14-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 row01-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 42
85.7%
02-Jan-2021 00:00:00 3
 
6.1%
03-Jan-2021 00:00:00 2
 
4.1%
05-Jan-2021 00:00:00 1
 
2.0%
14-Jan-2021 00:00:00 1
 
2.0%

Length

2023-12-10T23:44:29.034255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:44:29.132582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 49
50.0%
01-jan-2021 42
42.9%
02-jan-2021 3
 
3.1%
03-jan-2021 2
 
2.0%
05-jan-2021 1
 
1.0%
14-jan-2021 1
 
1.0%

ARVL_HMS
Categorical

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
13-Oct-2021 18:00:00
35 
13-Oct-2021 12:00:00
 
2
16-Jun-2021 06:00:00
 
1
07-Oct-2021 00:00:00
 
1
23-Apr-2021 06:00:00
 
1
Other values (9)
9 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique12 ?
Unique (%)24.5%

Sample

1st row13-Oct-2021 18:00:00
2nd row13-Oct-2021 18:00:00
3rd row16-Jun-2021 06:00:00
4th row13-Oct-2021 12:00:00
5th row13-Oct-2021 18:00:00

Common Values

ValueCountFrequency (%)
13-Oct-2021 18:00:00 35
71.4%
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%
12-Oct-2021 00:00:00 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 (4) 4
 
8.2%

Length

2023-12-10T23:44:29.230253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13-oct-2021 38
38.8%
18:00:00 38
38.8%
06:00:00 5
 
5.1%
12:00:00 3
 
3.1%
00:00:00 3
 
3.1%
12-oct-2021 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 (4) 4
 
4.1%

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.994172
Minimum-33.593399
Maximum59.402
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)2.0%
Memory size573.0 B
2023-12-10T23:44:29.337245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-33.593399
5-th percentile20.53906
Q147.651199
median51.312401
Q351.890099
95-th percentile53.15828
Maximum59.402
Range92.995399
Interquartile range (IQR)4.2389

Descriptive statistics

Standard deviation15.57764
Coefficient of variation (CV)0.33868726
Kurtosis14.605608
Mean45.994172
Median Absolute Deviation (MAD)0.598999
Skewness-3.5567851
Sum2253.7144
Variance242.66287
MonotonicityNot monotonic
2023-12-10T23:44:29.463733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
51.255199 1
 
2.0%
51.957901 1
 
2.0%
51.912399 1
 
2.0%
52.409901 1
 
2.0%
51.263302 1
 
2.0%
47.651199 1
 
2.0%
47.305698 1
 
2.0%
51.889702 1
 
2.0%
51.945202 1
 
2.0%
51.444901 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-33.593399 1
2.0%
4.63782 1
2.0%
20.2169 1
2.0%
21.022301 1
2.0%
23.136 1
2.0%
25.3904 1
2.0%
43.187901 1
2.0%
43.320099 1
2.0%
46.682701 1
2.0%
47.235001 1
2.0%
ValueCountFrequency (%)
59.402 1
2.0%
53.729301 1
2.0%
53.547001 1
2.0%
52.575199 1
2.0%
52.409901 1
2.0%
52.408699 1
2.0%
51.957901 1
2.0%
51.945202 1
2.0%
51.9324 1
2.0%
51.929199 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.139662
Minimum-17.998501
Maximum140.90601
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)6.1%
Memory size573.0 B
2023-12-10T23:44:29.583848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-17.998501
5-th percentile-5.03538
Q14.04421
median4.29188
Q34.835
95-th percentile50.7422
Maximum140.90601
Range158.90451
Interquartile range (IQR)0.79079

Descriptive statistics

Standard deviation23.318577
Coefficient of variation (CV)2.2997392
Kurtosis21.154845
Mean10.139662
Median Absolute Deviation (MAD)0.32443
Skewness4.1859591
Sum496.84341
Variance543.75605
MonotonicityNot monotonic
2023-12-10T23:44:29.710697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4.41231 1
 
2.0%
4.14538 1
 
2.0%
4.41777 1
 
2.0%
4.84186 1
 
2.0%
4.33545 1
 
2.0%
4.04421 1
 
2.0%
4.01756 1
 
2.0%
4.31671 1
 
2.0%
4.09071 1
 
2.0%
3.7217 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-17.998501 1
2.0%
-11.85 1
2.0%
-10.5251 1
2.0%
3.1992 1
2.0%
3.21527 1
2.0%
3.68228 1
2.0%
3.70181 1
2.0%
3.71266 1
2.0%
3.7217 1
2.0%
3.8 1
2.0%
ValueCountFrequency (%)
140.906006 1
2.0%
59.5411 1
2.0%
55.071999 1
2.0%
44.247501 1
2.0%
27.7614 1
2.0%
18.368999 1
2.0%
13.2451 1
2.0%
10.203 1
2.0%
9.96836 1
2.0%
6.60891 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.776854
Minimum-55.803398
Maximum52.575401
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)2.0%
Memory size573.0 B
2023-12-10T23:44:29.848300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-55.803398
5-th percentile17.07012
Q151.072399
median51.2556
Q351.819698
95-th percentile52.3815
Maximum52.575401
Range108.3788
Interquartile range (IQR)0.747299

Descriptive statistics

Standard deviation18.340816
Coefficient of variation (CV)0.40960484
Kurtosis19.070034
Mean44.776854
Median Absolute Deviation (MAD)0.564098
Skewness-3.9954125
Sum2194.0658
Variance336.38553
MonotonicityNot monotonic
2023-12-10T23:44:29.980418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
51.259998 1
 
2.0%
51.9249 1
 
2.0%
51.928101 1
 
2.0%
51.943401 1
 
2.0%
51.128101 1
 
2.0%
51.269402 1
 
2.0%
51.255501 1
 
2.0%
52.510502 1
 
2.0%
51.945202 1
 
2.0%
51.2486 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-55.803398 1
2.0%
3.98212 1
2.0%
14.4342 1
2.0%
21.024 1
2.0%
25.0313 1
2.0%
25.3901 1
2.0%
29.0243 1
2.0%
34.190399 1
2.0%
44.7103 1
2.0%
45.327702 1
2.0%
ValueCountFrequency (%)
52.575401 1
2.0%
52.510502 1
2.0%
52.4063 1
2.0%
52.344299 1
2.0%
52.152401 1
2.0%
51.945202 1
2.0%
51.943401 1
2.0%
51.928101 1
2.0%
51.9249 1
2.0%
51.909698 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2302949
Minimum-71.011902
Maximum128.509
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)4.1%
Memory size573.0 B
2023-12-10T23:44:30.107597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-71.011902
5-th percentile3.703298
Q14.28916
median4.37778
Q35.00827
95-th percentile52.347499
Maximum128.509
Range199.52091
Interquartile range (IQR)0.71911

Descriptive statistics

Standard deviation24.505105
Coefficient of variation (CV)2.6548561
Kurtosis13.57746
Mean9.2302949
Median Absolute Deviation (MAD)0.20209
Skewness2.1154106
Sum452.28445
Variance600.50018
MonotonicityNot monotonic
2023-12-10T23:44:30.235118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4.39037 1
 
2.0%
4.21934 1
 
2.0%
4.16942 1
 
2.0%
4.17569 1
 
2.0%
4.8092 1
 
2.0%
4.36555 1
 
2.0%
4.37778 1
 
2.0%
5.41308 1
 
2.0%
4.0907 1
 
2.0%
4.37623 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-71.011902 1
2.0%
-17.2987 1
2.0%
3.66235 1
2.0%
3.76472 1
2.0%
3.91386 1
2.0%
4.0907 1
2.0%
4.16942 1
2.0%
4.17569 1
2.0%
4.21934 1
2.0%
4.22817 1
2.0%
ValueCountFrequency (%)
128.509003 1
2.0%
55.07 1
2.0%
55.067699 1
2.0%
48.2672 1
2.0%
37.838902 1
2.0%
14.3012 1
2.0%
13.245 1
2.0%
8.69654 1
2.0%
8.51734 1
2.0%
8.03213 1
2.0%

ADDTI_RSTC
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3390079.6
Minimum0
Maximum13010300
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:30.379116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7690.808
Q1414582
median1981990
Q35813300
95-th percentile10549000
Maximum13010300
Range13010300
Interquartile range (IQR)5398718

Descriptive statistics

Standard deviation3736544.5
Coefficient of variation (CV)1.1021996
Kurtosis-0.18249298
Mean3390079.6
Median Absolute Deviation (MAD)1627004
Skewness1.0661591
Sum1.661139 × 108
Variance1.3961765 × 1013
MonotonicityNot monotonic
2023-12-10T23:44:30.511129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
398661.0 1
 
2.0%
8540490.0 1
 
2.0%
11125500.0 1
 
2.0%
5465260.0 1
 
2.0%
636657.0 1
 
2.0%
3578480.0 1
 
2.0%
2507550.0 1
 
2.0%
7960470.0 1
 
2.0%
10227400.0 1
 
2.0%
8532370.0 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
516.462 1
2.0%
6330.02 1
2.0%
9731.99 1
2.0%
57884.5 1
2.0%
85080.1 1
2.0%
182619.0 1
2.0%
228437.0 1
2.0%
313240.0 1
2.0%
354986.0 1
2.0%
ValueCountFrequency (%)
13010300.0 1
2.0%
11125500.0 1
2.0%
10763400.0 1
2.0%
10227400.0 1
2.0%
10021800.0 1
2.0%
8904460.0 1
2.0%
8771020.0 1
2.0%
8540490.0 1
2.0%
8532370.0 1
2.0%
7960470.0 1
2.0%

TOT_RSTC
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44463.237
Minimum0
Maximum523945
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:30.632756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile590.2396
Q111465.1
median19428.6
Q330715.3
95-th percentile128933.6
Maximum523945
Range523945
Interquartile range (IQR)19250.2

Descriptive statistics

Standard deviation89360.428
Coefficient of variation (CV)2.0097599
Kurtosis19.968252
Mean44463.237
Median Absolute Deviation (MAD)8670.2
Skewness4.2998331
Sum2178698.6
Variance7.9852861 × 109
MonotonicityNot monotonic
2023-12-10T23:44:30.762488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
7879.79 1
 
2.0%
19011.5 1
 
2.0%
12783.8 1
 
2.0%
19256.9 1
 
2.0%
22196.0 1
 
2.0%
15300.7 1
 
2.0%
6450.29 1
 
2.0%
48165.9 1
 
2.0%
24378.1 1
 
2.0%
21370.8 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
0.623804 1
2.0%
134.526 1
2.0%
1273.81 1
2.0%
1623.73 1
2.0%
4865.81 1
2.0%
6450.29 1
2.0%
7879.79 1
2.0%
8226.95 1
2.0%
8306.77 1
2.0%
ValueCountFrequency (%)
523945.0 1
2.0%
356943.0 1
2.0%
140898.0 1
2.0%
110987.0 1
2.0%
109222.0 1
2.0%
104153.0 1
2.0%
54372.7 1
2.0%
48165.9 1
2.0%
43179.8 1
2.0%
42795.4 1
2.0%

RL_POWER
Categorical

IMBALANCE 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
45 
6439420
 
1
5320880
 
1
3187580
 
1
3467850
 
1

Length

Max length7
Median length1
Mean length1.4897959
Min length1

Unique

Unique4 ?
Unique (%)8.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 45
91.8%
6439420 1
 
2.0%
5320880 1
 
2.0%
3187580 1
 
2.0%
3467850 1
 
2.0%

Length

2023-12-10T23:44:30.878150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:44:31.256177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 45
91.8%
6439420 1
 
2.0%
5320880 1
 
2.0%
3187580 1
 
2.0%
3467850 1
 
2.0%

FUEL_CNSMP_QTY
Categorical

IMBALANCE 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
45 
6724.73
 
1
5428.26
 
1
3254.32
 
1
3665.47
 
1

Length

Max length7
Median length3
Mean length3.3265306
Min length3

Unique

Unique4 ?
Unique (%)8.2%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 45
91.8%
6724.73 1
 
2.0%
5428.26 1
 
2.0%
3254.32 1
 
2.0%
3665.47 1
 
2.0%

Length

2023-12-10T23:44:31.356834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:44:31.451940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45
91.8%
6724.73 1
 
2.0%
5428.26 1
 
2.0%
3254.32 1
 
2.0%
3665.47 1
 
2.0%

CDBX
Categorical

IMBALANCE 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
45 
20940.8
 
1
16903.6
 
1
10133.9
 
1
11414.3
 
1

Length

Max length7
Median length3
Mean length3.3265306
Min length3

Unique

Unique4 ?
Unique (%)8.2%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 45
91.8%
20940.8 1
 
2.0%
16903.6 1
 
2.0%
10133.9 1
 
2.0%
11414.3 1
 
2.0%

Length

2023-12-10T23:44:31.554008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:44:31.651681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45
91.8%
20940.8 1
 
2.0%
16903.6 1
 
2.0%
10133.9 1
 
2.0%
11414.3 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
2023-12-10T23:44:31.755828image/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
2023-12-10T23:44:31.882130image/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_QTYCDBXRN
02054656900OLIEVINKER IVInland Motor Tanker liquid cargo type C10.566.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:0051.2551994.4123151.2599984.39037398661.07879.7900.00.02
12054798900NEW YORKTanker12.0125.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:0051.9291994.1933751.2650994.28703800677.035394.500.00.03
22054812900<NA><NA>0.00.00.0<NA>0.0HYUNDAI HEAVY INDUSTRIES1900001-Jan-2021 00:00:0016-Jun-2021 06:00:0047.60493.851.9096984.4202213010300.023612.100.00.04
32054880900GUATEMALAInland Motor Tanker liquid cargo type C10.282.00.0<NA>0.0<NA>2010170002-Jan-2021 00:00:0013-Oct-2021 12:00:0043.3200993.7018151.1996994.355488771020.015230.200.00.05
42054882900MAC 7Inland Motor Tanker7.840.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:0051.2625014.2691551.25424.22817435423.01623.7300.00.06
52054908900ROSSINIInland Motor Tanker liquid cargo type C11.4110.00.0<NA>0.0<NA>2010320001-Jan-2021 00:00:0013-Oct-2021 18:00:0051.3130994.2766151.2957994.32876354986.015580.500.00.07
62054912900ELISABETHSTADInland Motor Tanker liquid cargo type C15.0135.00.0<NA>0.0<NA>2010590001-Jan-2021 00:00:0013-Oct-2021 18:00:0052.4086994.83551.27144.329711267300.011465.100.00.08
72054956900ARTEGAInland Motor Tanker11.5110.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:0051.8900994.3673351.6921014.410771981990.043179.800.00.09
82054984900RIO Y MARInland Unknown8.280.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:0047.2350014.2787852.3442998.517342013590.023985.100.00.010
92055009900ZAMBEZIInland Motor Tanker liquid cargo type C10.281.20.0<NA>0.0<NA>2009170001-Jan-2021 00:00:0013-Oct-2021 18:00:0051.29994.2907451.2624024.3284257884.510758.400.00.011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOADDTI_RSTCTOT_RSTCRL_POWERFUEL_CNSMP_QTYCDBXRN
392092400009430210GOGLANDOil or Chemical Tanker16.091.950.0<NA>0.0<NA>2018445001-Jan-2021 00:00:0013-Oct-2021 18:00:0043.18790127.761451.12553.7647285080.1104153.000.00.041
402092490009254006SEA DWELLEROil or Chemical Tanker14.087.310.0<NA>0.0<NA>2002342001-Jan-2021 00:00:0013-Oct-2021 18:00:0021.022301-17.99850114.4342-17.2987228437.0110987.000.00.042
412108570009277759N.MARSOIL PRODUCTS TANKER32.2219.020.9<NA>14.4HYUNDAI HEAVY INDUSTRIES20047499901-Jan-2021 00:00:0008-Jan-2021 06:00:0025.390455.07199925.390155.07516.4620.62380400.00.043
422109790009277773NORDNEPTUNCrude Oil Tanker32.2219.020.9<NA>13.6HYUNDAI HEAVY INDUSTRIES20047499901-Jan-2021 00:00:0013-Oct-2021 18:00:0023.13659.541129.024348.2672387533.0140898.031875803254.3210133.944
432110000010HAMBURG HORIZONTanker - Hazard C (Minor)30.0310.00.0<NA>0.0<NA>0014-Jan-2021 00:00:0028-Sep-2021 00:00:0053.5470019.9683621.0243.913862473200.018798.100.00.045
442110005980ENTEInland Ferry2.58.50.0<NA>0.0<NA>0002-Jan-2021 00:00:0010-Sep-2021 18:00:0052.57519913.245152.57540113.245313240.04865.8100.00.046
4521112345699999999KUNDBInland Motor Tanker liquid cargo type C22.8112.50.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:0059.40218.36899945.32770214.30121677970.023585.500.00.047
462111298000EDGAR JAEGERSInland Motor Tanker11.5110.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:0051.47063.7126651.8916024.271158904460.030715.300.00.048
472111350009298193SEASHARKOil Products Tanker28.0168.016.8<NA>11.1LINDENAU SHIPYARD20043230201-Jan-2021 00:00:0013-Oct-2021 18:00:0053.729301-10.525144.710337.8389021012490.0109222.034678503665.4711414.349
482111419004812830JANINA RInland Motor Tanker11.5110.00.0<NA>0.0<NA>0001-Jan-2021 00:00:0013-Oct-2021 18:00:0051.63436.6089148.7832988.03213971904.054372.700.00.050