Overview

Dataset statistics

Number of variables7
Number of observations49
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory62.7 B

Variable types

Text1
Numeric3
DateTime2
Categorical1

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000441

Alerts

SHIP_CNT is highly overall correlated with FRGHT_CNVNC_QTY and 1 other fieldsHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with SHIP_CNT and 1 other fieldsHigh correlation
RN is highly overall correlated with CRG_TYPHigh correlation
CRG_TYP is highly overall correlated with SHIP_CNT and 2 other fieldsHigh correlation
SHIP_KIND has 1 (2.0%) missing valuesMissing
RN has unique valuesUnique
FRGHT_CNVNC_QTY has 2 (4.1%) zerosZeros

Reproduction

Analysis started2023-12-10 14:49:03.894292
Analysis finished2023-12-10 14:49:04.992568
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHIP_KIND
Text

MISSING 

Distinct44
Distinct (%)91.7%
Missing1
Missing (%)2.0%
Memory size524.0 B
2023-12-10T23:49:05.111305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length27
Mean length17.25
Min length3

Characters and Unicode

Total characters828
Distinct characters50
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)83.3%

Sample

1st rowLPG Tanker
2nd rowProduct Tankers
3rd rowLPG TANKER
4th rowLNG TANKER
5th rowOil Products Tanker
ValueCountFrequency (%)
tanker 35
25.5%
inland 8
 
5.8%
oil 7
 
5.1%
5
 
3.6%
hazard 5
 
3.6%
cargo 4
 
2.9%
chemical 4
 
2.9%
pushtow 4
 
2.9%
barges 3
 
2.2%
or 3
 
2.2%
Other values (44) 59
43.1%
2023-12-10T23:49:05.424982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
 
10.7%
a 71
 
8.6%
e 65
 
7.9%
r 64
 
7.7%
n 57
 
6.9%
T 42
 
5.1%
k 31
 
3.7%
o 27
 
3.3%
l 26
 
3.1%
i 24
 
2.9%
Other values (40) 332
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 513
62.0%
Uppercase Letter 210
25.4%
Space Separator 89
 
10.7%
Dash Punctuation 5
 
0.6%
Close Punctuation 4
 
0.5%
Open Punctuation 4
 
0.5%
Other Punctuation 2
 
0.2%
Decimal Number 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 71
13.8%
e 65
12.7%
r 64
12.5%
n 57
11.1%
k 31
 
6.0%
o 27
 
5.3%
l 26
 
5.1%
i 24
 
4.7%
s 22
 
4.3%
t 20
 
3.9%
Other values (12) 106
20.7%
Uppercase Letter
ValueCountFrequency (%)
T 42
20.0%
A 15
 
7.1%
I 15
 
7.1%
C 15
 
7.1%
P 13
 
6.2%
E 12
 
5.7%
N 11
 
5.2%
O 11
 
5.2%
R 10
 
4.8%
L 10
 
4.8%
Other values (12) 56
26.7%
Space Separator
ValueCountFrequency (%)
89
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 723
87.3%
Common 105
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 71
 
9.8%
e 65
 
9.0%
r 64
 
8.9%
n 57
 
7.9%
T 42
 
5.8%
k 31
 
4.3%
o 27
 
3.7%
l 26
 
3.6%
i 24
 
3.3%
s 22
 
3.0%
Other values (34) 294
40.7%
Common
ValueCountFrequency (%)
89
84.8%
- 5
 
4.8%
) 4
 
3.8%
( 4
 
3.8%
/ 2
 
1.9%
2 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89
 
10.7%
a 71
 
8.6%
e 65
 
7.9%
r 64
 
7.7%
n 57
 
6.9%
T 42
 
5.1%
k 31
 
3.7%
o 27
 
3.3%
l 26
 
3.1%
i 24
 
2.9%
Other values (40) 332
40.1%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360.71429
Minimum1
Maximum4407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:05.543564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median13
Q3238
95-th percentile2197.8
Maximum4407
Range4406
Interquartile range (IQR)234

Descriptive statistics

Standard deviation922.17668
Coefficient of variation (CV)2.5565294
Kurtosis12.34059
Mean360.71429
Median Absolute Deviation (MAD)12
Skewness3.5361256
Sum17675
Variance850409.83
MonotonicityNot monotonic
2023-12-10T23:49:05.654616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 7
 
14.3%
2 3
 
6.1%
4 2
 
4.1%
9 2
 
4.1%
12 2
 
4.1%
5 2
 
4.1%
3 2
 
4.1%
20 2
 
4.1%
238 2
 
4.1%
6 2
 
4.1%
Other values (22) 23
46.9%
ValueCountFrequency (%)
1 7
14.3%
2 3
6.1%
3 2
 
4.1%
4 2
 
4.1%
5 2
 
4.1%
6 2
 
4.1%
8 1
 
2.0%
9 2
 
4.1%
10 1
 
2.0%
12 2
 
4.1%
ValueCountFrequency (%)
4407 1
2.0%
3986 1
2.0%
2769 1
2.0%
1341 1
2.0%
873 1
2.0%
859 1
2.0%
438 1
2.0%
370 1
2.0%
314 1
2.0%
294 1
2.0%

DPTR_HMS
Date

Distinct39
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:00
Maximum2022-04-02 11:50:45
2023-12-10T23:49:05.794268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:05.932471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)

ARVL_HMS
Date

Distinct36
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-10-13 23:55:01
Maximum2022-07-17 22:00:23
2023-12-10T23:49:06.069283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:06.188293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)

CRG_TYP
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
<NA>
33 
0
16 

Length

Max length4
Median length4
Mean length3.0204082
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 33
67.3%
0 16
32.7%

Length

2023-12-10T23:49:06.666289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:06.760895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 33
67.3%
0 16
32.7%

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1305313 × 1011
Minimum0
Maximum1.74132 × 1013
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:06.863853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5299400
Q11.29484 × 108
median9.47039 × 108
Q32.28577 × 1010
95-th percentile1.0082992 × 1012
Maximum1.74132 × 1013
Range1.74132 × 1013
Interquartile range (IQR)2.2728216 × 1010

Descriptive statistics

Standard deviation2.536204 × 1012
Coefficient of variation (CV)4.9433554
Kurtosis43.434882
Mean5.1305313 × 1011
Median Absolute Deviation (MAD)9.454352 × 108
Skewness6.4756479
Sum2.5139603 × 1013
Variance6.4323305 × 1024
MonotonicityNot monotonic
2023-12-10T23:49:07.027062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 2
 
4.1%
178789000000 1
 
2.0%
41796300 1
 
2.0%
43834600 1
 
2.0%
644821000 1
 
2.0%
14829500000 1
 
2.0%
141198000 1
 
2.0%
439227000 1
 
2.0%
1134240000 1
 
2.0%
155991000000 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
0 2
4.1%
1603800 1
2.0%
10842800 1
2.0%
28136400 1
2.0%
36595000 1
2.0%
41796300 1
2.0%
43834600 1
2.0%
63392900 1
2.0%
67650800 1
2.0%
109898000 1
2.0%
ValueCountFrequency (%)
17413200000000 1
2.0%
3985980000000 1
2.0%
1169830000000 1
2.0%
766003000000 1
2.0%
601860000000 1
2.0%
291490000000 1
2.0%
219121000000 1
2.0%
178789000000 1
2.0%
163357000000 1
2.0%
155991000000 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
2023-12-10T23:49:07.178153image/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
2023-12-10T23:49:07.320361image/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

2023-12-10T23:49:04.618276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:04.177020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:04.392186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:04.684789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:04.245104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:04.461997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:04.761494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:04.322477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:04.544212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:49:07.414382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
SHIP_KIND1.0000.0000.9800.9760.0000.610
SHIP_CNT0.0001.0000.0000.0000.8210.000
DPTR_HMS0.9800.0001.0000.9920.0000.906
ARVL_HMS0.9760.0000.9921.0000.0000.765
FRGHT_CNVNC_QTY0.0000.8210.0000.0001.0000.000
RN0.6100.0000.9060.7650.0001.000
2023-12-10T23:49:07.533883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTFRGHT_CNVNC_QTYRNCRG_TYP
SHIP_CNT1.0000.866-0.1801.000
FRGHT_CNVNC_QTY0.8661.000-0.3261.000
RN-0.180-0.3261.0001.000
CRG_TYP1.0001.0001.0001.000

Missing values

2023-12-10T23:49:04.861971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:49:04.957422image/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

SHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSCRG_TYPFRGHT_CNVNC_QTYRN
0LPG Tanker87301-Jan-2022 00:00:0117-Jul-2022 22:00:14<NA>1787890000002
1Product Tankers440701-Jan-2022 00:00:0117-Jul-2022 22:00:23<NA>39859800000003
2LPG TANKER28301-Jan-2022 00:00:1117-Jul-2022 22:00:17<NA>7660030000004
3LNG TANKER22401-Jan-2022 00:00:0317-Jul-2022 22:00:18<NA>11698300000005
4Oil Products Tanker17301-Jan-2022 00:00:0717-Jul-2022 22:00:08<NA>56580400006
5Chemical Tanker29401-Jan-2022 00:00:1617-Jul-2022 22:00:18<NA>228577000007
6Oil or Chemical Tanker85901-Jan-2022 00:00:0817-Jul-2022 22:00:14<NA>849266000008
7LNG Tanker22401-Jan-2022 00:00:1317-Jul-2022 22:00:18<NA>2191210000009
8CHEMICAL TANKER25701-Jan-2022 00:00:1517-Jul-2022 22:00:19<NA>16335700000010
9OIL/CHEMICAL TANKER43801-Jan-2022 00:00:1217-Jul-2022 22:00:21<NA>29149000000011
SHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSCRG_TYPFRGHT_CNVNC_QTYRN
39Fishing9401-Jan-2021 00:00:0113-Oct-2021 23:58:030232862000041
40Fishing Vessel1301-Jan-2021 00:00:0513-Oct-2021 23:59:05094703900042
41Tanker - Hazard A (Major)23801-Jan-2021 00:00:0013-Oct-2021 23:59:050468861000043
42Tug6401-Jan-2021 00:00:0113-Oct-2021 23:59:050205947000044
43Inland Pushtow four barges at least one tanker401-Jan-2021 00:00:0413-Oct-2021 23:59:0403659500045
44Inland Motor Tanker37001-Jan-2021 00:00:0013-Oct-2021 23:59:050446976000046
45Inland Pushtow one cargo barge501-Jan-2021 00:00:1913-Oct-2021 23:56:0001084280047
46Inland Pushtow two cargo barges1001-Jan-2021 00:00:0113-Oct-2021 23:59:02011157800048
47Inland Pushtow three cargo barges221-Jan-2021 07:36:1713-Oct-2021 23:55:0102813640049
48Crude Oil Tanker276901-Jan-2021 00:00:0013-Oct-2021 23:59:0501741320000000050