Overview

Dataset statistics

Number of variables9
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory79.7 B

Variable types

Numeric4
Text1
Categorical2
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000447

Alerts

CRG_TYP has constant value ""Constant
RANK is highly overall correlated with FRGHT_CNVNC_QTY and 1 other fieldsHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with RANK and 1 other fieldsHigh correlation
RN is highly overall correlated with RANK and 1 other fieldsHigh correlation
RANK has unique valuesUnique
DPTR_HMS has unique valuesUnique
FRGHT_CNVNC_QTY has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:38:56.240007
Analysis finished2023-12-10 14:38:57.823546
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RANK
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326
Minimum302
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:38:57.883545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum302
5-th percentile304.4
Q1314
median326
Q3338
95-th percentile347.6
Maximum350
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.043830338
Kurtosis-1.2
Mean326
Median Absolute Deviation (MAD)12
Skewness0
Sum15974
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:38:58.022725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
302 1
 
2.0%
339 1
 
2.0%
329 1
 
2.0%
330 1
 
2.0%
331 1
 
2.0%
332 1
 
2.0%
333 1
 
2.0%
334 1
 
2.0%
335 1
 
2.0%
336 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
302 1
2.0%
303 1
2.0%
304 1
2.0%
305 1
2.0%
306 1
2.0%
307 1
2.0%
308 1
2.0%
309 1
2.0%
310 1
2.0%
311 1
2.0%
ValueCountFrequency (%)
350 1
2.0%
349 1
2.0%
348 1
2.0%
347 1
2.0%
346 1
2.0%
345 1
2.0%
344 1
2.0%
343 1
2.0%
342 1
2.0%
341 1
2.0%

ARVL_CN_NM
Text

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:38:58.181507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length20
Mean length9.3061224
Min length4

Characters and Unicode

Total characters456
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)71.4%

Sample

1st rowAlgeria
2nd rowSeychelles
3rd rowCuracao
4th rowGermany
5th rowTaiwan
ValueCountFrequency (%)
south 3
 
4.3%
algeria 2
 
2.9%
arabia 2
 
2.9%
netherlands 2
 
2.9%
sri 2
 
2.9%
lanka 2
 
2.9%
africa 2
 
2.9%
india 2
 
2.9%
china 2
 
2.9%
the 2
 
2.9%
Other values (46) 48
69.6%
2023-12-10T23:38:58.457755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 66
14.5%
e 42
 
9.2%
i 38
 
8.3%
n 38
 
8.3%
r 25
 
5.5%
20
 
4.4%
o 20
 
4.4%
d 18
 
3.9%
t 17
 
3.7%
u 15
 
3.3%
Other values (37) 157
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365
80.0%
Uppercase Letter 68
 
14.9%
Space Separator 20
 
4.4%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 66
18.1%
e 42
11.5%
i 38
10.4%
n 38
10.4%
r 25
 
6.8%
o 20
 
5.5%
d 18
 
4.9%
t 17
 
4.7%
u 15
 
4.1%
l 15
 
4.1%
Other values (14) 71
19.5%
Uppercase Letter
ValueCountFrequency (%)
S 12
17.6%
A 9
13.2%
C 8
11.8%
G 5
 
7.4%
N 4
 
5.9%
I 4
 
5.9%
L 3
 
4.4%
V 3
 
4.4%
P 3
 
4.4%
B 2
 
2.9%
Other values (9) 15
22.1%
Space Separator
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 433
95.0%
Common 23
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 66
15.2%
e 42
 
9.7%
i 38
 
8.8%
n 38
 
8.8%
r 25
 
5.8%
o 20
 
4.6%
d 18
 
4.2%
t 17
 
3.9%
u 15
 
3.5%
l 15
 
3.5%
Other values (33) 139
32.1%
Common
ValueCountFrequency (%)
20
87.0%
( 1
 
4.3%
) 1
 
4.3%
- 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 66
14.5%
e 42
 
9.2%
i 38
 
8.3%
n 38
 
8.3%
r 25
 
5.5%
20
 
4.4%
o 20
 
4.4%
d 18
 
3.9%
t 17
 
3.7%
u 15
 
3.3%
Other values (37) 157
34.4%

SHIP_KIND
Categorical

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
Oil or Chemical Tanker
8 
Crude Oil Tanker
8 
Oil Products Tanker
5 
LPG Tanker
5 
LNG Tanker
4 
Other values (10)
19 

Length

Max length30
Median length19
Mean length15.387755
Min length4

Unique

Unique5 ?
Unique (%)10.2%

Sample

1st rowLNG Tanker
2nd rowOil or Chemical Tanker
3rd rowCrude Oil Tanker
4th rowOil Products Tanker
5th rowOil Products Tanker

Common Values

ValueCountFrequency (%)
Oil or Chemical Tanker 8
16.3%
Crude Oil Tanker 8
16.3%
Oil Products Tanker 5
10.2%
LPG Tanker 5
10.2%
LNG Tanker 4
8.2%
OIL PRODUCTS TANKER 4
8.2%
<NA> 3
 
6.1%
Chemical Tanker 3
 
6.1%
CRUDE OIL TANKER 2
 
4.1%
Shuttle Tanker 2
 
4.1%
Other values (5) 5
10.2%

Length

2023-12-10T23:38:58.576566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tanker 43
32.8%
oil 27
20.6%
chemical 11
 
8.4%
crude 10
 
7.6%
or 9
 
6.9%
products 9
 
6.9%
lpg 6
 
4.6%
lng 4
 
3.1%
na 3
 
2.3%
shuttle 2
 
1.5%
Other values (7) 7
 
5.3%

SHIP_CNT
Real number (ℝ)

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.469388
Minimum1
Maximum523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:38:58.671911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q336
95-th percentile341.2
Maximum523
Range522
Interquartile range (IQR)33

Descriptive statistics

Standard deviation114.892
Coefficient of variation (CV)2.1487435
Kurtosis8.9193975
Mean53.469388
Median Absolute Deviation (MAD)7
Skewness3.0516451
Sum2620
Variance13200.171
MonotonicityNot monotonic
2023-12-10T23:38:58.773959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3 5
 
10.2%
2 4
 
8.2%
8 4
 
8.2%
1 4
 
8.2%
5 3
 
6.1%
12 2
 
4.1%
4 2
 
4.1%
13 2
 
4.1%
9 2
 
4.1%
155 1
 
2.0%
Other values (20) 20
40.8%
ValueCountFrequency (%)
1 4
8.2%
2 4
8.2%
3 5
10.2%
4 2
 
4.1%
5 3
6.1%
6 1
 
2.0%
7 1
 
2.0%
8 4
8.2%
9 2
 
4.1%
11 1
 
2.0%
ValueCountFrequency (%)
523 1
2.0%
448 1
2.0%
402 1
2.0%
250 1
2.0%
155 1
2.0%
143 1
2.0%
95 1
2.0%
80 1
2.0%
58 1
2.0%
50 1
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:05
Maximum2021-09-05 13:46:01
2023-12-10T23:38:58.882154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:58.995580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Date

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-02-04 21:25:12
Maximum2021-10-13 23:59:04
2023-12-10T23:38:59.114043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:59.475195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

CRG_TYP
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

2023-12-10T23:38:59.579286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:38:59.655606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0971059 × 108
Minimum6.75582 × 108
Maximum9.49566 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:38:59.741625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.75582 × 108
5-th percentile7.039204 × 108
Q17.46776 × 108
median7.93909 × 108
Q38.78176 × 108
95-th percentile9.278504 × 108
Maximum9.49566 × 108
Range2.73984 × 108
Interquartile range (IQR)1.314 × 108

Descriptive statistics

Standard deviation77434059
Coefficient of variation (CV)0.095631772
Kurtosis-1.2327444
Mean8.0971059 × 108
Median Absolute Deviation (MAD)67024000
Skewness0.15119614
Sum3.9675819 × 1010
Variance5.9960335 × 1015
MonotonicityStrictly decreasing
2023-12-10T23:38:59.865082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
949566000 1
 
2.0%
740723000 1
 
2.0%
782264000 1
 
2.0%
772788000 1
 
2.0%
767085000 1
 
2.0%
766128000 1
 
2.0%
763132000 1
 
2.0%
761310000 1
 
2.0%
759748000 1
 
2.0%
753041000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
675582000 1
2.0%
690253000 1
2.0%
698828000 1
2.0%
711559000 1
2.0%
713139000 1
2.0%
714519000 1
2.0%
718025000 1
2.0%
724634000 1
2.0%
729221000 1
2.0%
731464000 1
2.0%
ValueCountFrequency (%)
949566000 1
2.0%
934857000 1
2.0%
929346000 1
2.0%
925607000 1
2.0%
925600000 1
2.0%
924405000 1
2.0%
904228000 1
2.0%
902510000 1
2.0%
901085000 1
2.0%
882649000 1
2.0%

RN
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:38:59.981440image/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
2023-12-10T23:39:00.101660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

Interactions

2023-12-10T23:38:57.384284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:56.549498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:56.825188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:57.101057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:57.452535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:56.613966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:56.895937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:57.170280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:57.516993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:56.679389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:56.962582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:57.243375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:57.583832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:56.750770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:57.035972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:57.314131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:39:00.181373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKARVL_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
RANK1.0000.6590.1960.0001.0000.9380.9761.000
ARVL_CN_NM0.6591.0000.0000.0001.0000.9820.8170.454
SHIP_KIND0.1960.0001.0000.0001.0000.2300.1860.349
SHIP_CNT0.0000.0000.0001.0001.0001.0000.4180.000
DPTR_HMS1.0001.0001.0001.0001.0001.0001.0001.000
ARVL_HMS0.9380.9820.2301.0001.0001.0000.9480.936
FRGHT_CNVNC_QTY0.9760.8170.1860.4181.0000.9481.0000.978
RN1.0000.4540.3490.0001.0000.9360.9781.000
2023-12-10T23:39:00.277499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRNSHIP_KIND
RANK1.000-0.152-1.0001.0000.000
SHIP_CNT-0.1521.0000.152-0.1520.000
FRGHT_CNVNC_QTY-1.0000.1521.000-1.0000.000
RN1.000-0.152-1.0001.0000.000
SHIP_KIND0.0000.0000.0000.0001.000

Missing values

2023-12-10T23:38:57.673376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:38:57.781446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RANKARVL_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSCRG_TYPFRGHT_CNVNC_QTYRN
0302AlgeriaLNG Tanker3502-Jan-2021 17:06:1207-Oct-2021 22:11:0109495660002
1303SeychellesOil or Chemical Tanker901-Jan-2021 00:08:0415-Sep-2021 07:03:0409348570003
2304CuracaoCrude Oil Tanker801-Jan-2021 05:45:0228-Jul-2021 07:30:3709293460004
3305GermanyOil Products Tanker14301-Jan-2021 00:01:2413-Oct-2021 23:57:0409256070005
4306TaiwanOil Products Tanker1315-Jan-2021 03:44:0609-Oct-2021 16:55:0309256000006
5307AlgeriaOil or Chemical Tanker9501-Jan-2021 01:13:2513-Oct-2021 23:48:0509244050007
6308ReunionLNG Tanker715-Jan-2021 09:21:4904-Sep-2021 05:06:0209042280008
7309United KingdomLPG Tanker52301-Jan-2021 00:47:1213-Oct-2021 23:45:0009025100009
8310NetherlandsLPG Tanker40201-Jan-2021 00:00:0713-Oct-2021 20:25:00090108500010
9311Sri LankaCRUDE OIL TANKER205-Mar-2021 23:56:3324-Mar-2021 20:54:23088264900011
RANKARVL_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSCRG_TYPFRGHT_CNVNC_QTYRN
39341Saudi ArabiaLPG Tanker8002-Jan-2021 19:41:1713-Oct-2021 23:55:00073146400041
40342ChinaLPG TANKER307-Jan-2021 01:55:0723-May-2021 16:25:45072922100042
41343NetherlandsChemical Tanker44801-Jan-2021 00:01:0813-Oct-2021 21:45:04072463400043
42344Cote dIvoire (Ivory Coast)Crude Oil Tanker222-Apr-2021 23:41:2117-Jul-2021 12:40:03071802500044
43345Guadeloupe-MartiniqueLNG Tanker606-Jan-2021 06:11:4613-Oct-2021 12:39:05071451900045
44346New CaledoniaOil or Chemical Tanker1303-Feb-2021 12:22:3408-Oct-2021 20:52:00071313900046
45347South AfricaChemical Tanker309-Jan-2021 14:15:3201-Sep-2021 12:48:02071155900047
46348GabonCrude Oil Tanker516-Jan-2021 16:04:4013-Oct-2021 23:34:02069882800048
47349PolandOil or Chemical Tanker15501-Jan-2021 00:04:4313-Oct-2021 00:40:00069025300049
48350Saudi Arabia<NA>1601-Jan-2021 17:40:0313-Oct-2021 23:58:03067558200050