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_000437

Alerts

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

Reproduction

Analysis started2023-12-10 14:26:57.337078
Analysis finished2023-12-10 14:26:58.939096
Duration1.6 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
2023-12-10T23:26:59.002250image/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
2023-12-10T23:26:59.112190image/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%

DPTR_CN_NM
Text

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:26:59.298525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length17
Mean length8.5102041
Min length4

Characters and Unicode

Total characters417
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)69.4%

Sample

1st rowNorway
2nd rowSaudi Arabia
3rd rowGermany
4th rowThe Bahamas
5th rowNigeria
ValueCountFrequency (%)
iran 3
 
4.6%
nigeria 2
 
3.1%
papua 2
 
3.1%
united 2
 
3.1%
norway 2
 
3.1%
germany 2
 
3.1%
netherlands 2
 
3.1%
malaysia 2
 
3.1%
new 2
 
3.1%
guinea 2
 
3.1%
Other values (43) 44
67.7%
2023-12-10T23:26:59.590813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 72
17.3%
n 35
 
8.4%
i 32
 
7.7%
e 28
 
6.7%
r 28
 
6.7%
o 20
 
4.8%
16
 
3.8%
u 15
 
3.6%
d 14
 
3.4%
s 14
 
3.4%
Other values (33) 143
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 334
80.1%
Uppercase Letter 65
 
15.6%
Space Separator 16
 
3.8%
Other Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 72
21.6%
n 35
10.5%
i 32
9.6%
e 28
 
8.4%
r 28
 
8.4%
o 20
 
6.0%
u 15
 
4.5%
d 14
 
4.2%
s 14
 
4.2%
l 13
 
3.9%
Other values (10) 63
18.9%
Uppercase Letter
ValueCountFrequency (%)
S 8
12.3%
N 8
12.3%
I 7
10.8%
G 6
9.2%
T 5
7.7%
P 5
7.7%
A 5
7.7%
B 4
 
6.2%
M 3
 
4.6%
U 2
 
3.1%
Other values (10) 12
18.5%
Space Separator
ValueCountFrequency (%)
16
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 399
95.7%
Common 18
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 72
18.0%
n 35
 
8.8%
i 32
 
8.0%
e 28
 
7.0%
r 28
 
7.0%
o 20
 
5.0%
u 15
 
3.8%
d 14
 
3.5%
s 14
 
3.5%
l 13
 
3.3%
Other values (30) 128
32.1%
Common
ValueCountFrequency (%)
16
88.9%
& 1
 
5.6%
- 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 72
17.3%
n 35
 
8.4%
i 32
 
7.7%
e 28
 
6.7%
r 28
 
6.7%
o 20
 
4.8%
16
 
3.8%
u 15
 
3.6%
d 14
 
3.4%
s 14
 
3.4%
Other values (33) 143
34.3%

SHIP_KIND
Categorical

Distinct16
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
Crude Oil Tanker
8 
LPG Tanker
7 
Oil or Chemical Tanker
6 
Oil Products Tanker
5 
<NA>
4 
Other values (11)
19 

Length

Max length25
Median length19
Mean length14.673469
Min length4

Unique

Unique5 ?
Unique (%)10.2%

Sample

1st rowOil Products Tanker
2nd row<NA>
3rd rowChemical Tanker
4th rowContainer Ship
5th rowLNG Tanker

Common Values

ValueCountFrequency (%)
Crude Oil Tanker 8
16.3%
LPG Tanker 7
14.3%
Oil or Chemical Tanker 6
12.2%
Oil Products Tanker 5
10.2%
<NA> 4
8.2%
LNG Tanker 4
8.2%
Chemical Tanker 2
 
4.1%
Container Ship 2
 
4.1%
Asphalt or Bitumen Tanker 2
 
4.1%
OIL/CHEMICAL TANKER 2
 
4.1%
Other values (6) 7
14.3%

Length

2023-12-10T23:26:59.702615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tanker 42
33.9%
oil 22
17.7%
crude 9
 
7.3%
or 8
 
6.5%
chemical 8
 
6.5%
lpg 7
 
5.6%
products 7
 
5.6%
lng 4
 
3.2%
na 4
 
3.2%
container 2
 
1.6%
Other values (7) 11
 
8.9%

SHIP_CNT
Real number (ℝ)

Distinct33
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.163265
Minimum4
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:59.794675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q111
median16
Q352
95-th percentile128
Maximum200
Range196
Interquartile range (IQR)41

Descriptive statistics

Standard deviation46.542251
Coefficient of variation (CV)1.188416
Kurtosis3.4483568
Mean39.163265
Median Absolute Deviation (MAD)9
Skewness1.9350132
Sum1919
Variance2166.1811
MonotonicityNot monotonic
2023-12-10T23:26:59.898064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
11 4
 
8.2%
13 3
 
6.1%
8 3
 
6.1%
15 3
 
6.1%
7 3
 
6.1%
16 2
 
4.1%
6 2
 
4.1%
29 2
 
4.1%
33 2
 
4.1%
14 2
 
4.1%
Other values (23) 23
46.9%
ValueCountFrequency (%)
4 1
 
2.0%
5 1
 
2.0%
6 2
4.1%
7 3
6.1%
8 3
6.1%
9 1
 
2.0%
11 4
8.2%
12 1
 
2.0%
13 3
6.1%
14 2
4.1%
ValueCountFrequency (%)
200 1
2.0%
184 1
2.0%
138 1
2.0%
113 1
2.0%
112 1
2.0%
107 1
2.0%
96 1
2.0%
83 1
2.0%
74 1
2.0%
72 1
2.0%

DPTR_HMS
Date

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:07
Maximum2021-09-19 19:20:01
2023-12-10T23:27:00.006864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:00.124475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

ARVL_HMS
Date

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-03-12 12:43:15
Maximum2021-10-13 23:59:05
2023-12-10T23:27:00.228099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:00.358291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)

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

2023-12-10T23:27:00.489457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:27:00.579017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

FRGHT_CNVNC_QTY_TONM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean950551.86
Minimum831776
Maximum1123380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:27:00.950049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum831776
5-th percentile833609
Q1879322
median940781
Q31012820
95-th percentile1085774
Maximum1123380
Range291604
Interquartile range (IQR)133498

Descriptive statistics

Standard deviation81648.298
Coefficient of variation (CV)0.08589568
Kurtosis-0.91477086
Mean950551.86
Median Absolute Deviation (MAD)66849
Skewness0.17284458
Sum46577041
Variance6.6664446 × 109
MonotonicityStrictly decreasing
2023-12-10T23:27:01.090153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1123380 1
 
2.0%
875222 1
 
2.0%
937164 1
 
2.0%
935890 1
 
2.0%
931458 1
 
2.0%
931073 1
 
2.0%
927624 1
 
2.0%
909908 1
 
2.0%
908515 1
 
2.0%
898962 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
831776 1
2.0%
832530 1
2.0%
833407 1
2.0%
833912 1
2.0%
835470 1
2.0%
838547 1
2.0%
839820 1
2.0%
843854 1
2.0%
851008 1
2.0%
864680 1
2.0%
ValueCountFrequency (%)
1123380 1
2.0%
1093000 1
2.0%
1086110 1
2.0%
1085270 1
2.0%
1072440 1
2.0%
1047750 1
2.0%
1044810 1
2.0%
1044620 1
2.0%
1044260 1
2.0%
1024490 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
2023-12-10T23:27:01.239094image/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
2023-12-10T23:27:01.393229image/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

2023-12-10T23:26:58.493065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:57.707929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:57.971260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.241612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.554878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:57.769532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.038656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.301841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.623489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:57.847156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.111642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.370341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.693882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:57.908962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.177409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:58.430135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:27:01.487396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKDPTR_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
RANK1.0000.9110.2140.0000.9200.8760.9221.000
DPTR_CN_NM0.9111.0000.0000.9230.9800.9700.0000.904
SHIP_KIND0.2140.0001.0000.0000.9870.9330.6180.000
SHIP_CNT0.0000.9230.0001.0000.9070.8020.0000.134
DPTR_HMS0.9200.9800.9870.9071.0000.9870.9260.936
ARVL_HMS0.8760.9700.9330.8020.9871.0000.0000.804
FRGHT_CNVNC_QTY_TONM0.9220.0000.6180.0000.9260.0001.0000.970
RN1.0000.9040.0000.1340.9360.8040.9701.000
2023-12-10T23:27:01.592542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTY_TONMRNSHIP_KIND
RANK1.0000.019-1.0001.0000.000
SHIP_CNT0.0191.000-0.0190.0190.000
FRGHT_CNVNC_QTY_TONM-1.000-0.0191.000-1.0000.250
RN1.0000.019-1.0001.0000.000
SHIP_KIND0.0000.0000.2500.0001.000

Missing values

2023-12-10T23:26:58.791936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:26:58.898294image/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

RANKDPTR_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSCRG_TYPFRGHT_CNVNC_QTY_TONMRN
0302NorwayOil Products Tanker2114-Jan-2021 14:08:0913-Oct-2021 23:56:04011233802
1303Saudi Arabia<NA>1506-Jan-2021 17:44:5205-Oct-2021 15:22:01010930003
2304GermanyChemical Tanker20002-Jan-2021 13:40:1413-Oct-2021 23:58:03010861104
3305The BahamasContainer Ship1311-Jan-2021 08:08:3013-Oct-2021 22:17:02010852705
4306NigeriaLNG Tanker1601-Jan-2021 00:00:3607-Oct-2021 10:20:01010724406
5307CuracaoCrude Oil Tanker804-Jan-2021 01:43:3130-Jul-2021 23:16:53010477507
6308PolandCrude Oil Tanker1111-Jan-2021 20:35:0713-Oct-2021 23:59:02010448108
7309IranCRUDE OIL TANKER606-Jan-2021 12:50:0412-Mar-2021 12:43:15010446209
8310NorwayLNG Tanker7204-Jan-2021 00:50:3613-Oct-2021 23:33:030104426010
9311IranTanker419-Sep-2021 19:20:0103-Oct-2021 20:23:050102449011
RANKDPTR_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSCRG_TYPFRGHT_CNVNC_QTY_TONMRN
39341SingaporeOil Products Tanker1409-Feb-2021 04:42:3816-Sep-2021 01:30:01086468041
40342South KoreaLPG Tanker5802-Jan-2021 00:49:3213-Oct-2021 19:55:04085100842
41343Netherlands AntillesOil Products Tanker6805-Jan-2021 19:08:4409-Oct-2021 05:48:04084385443
42344BruneiCrude Oil Tanker1119-Feb-2021 07:03:2310-Oct-2021 01:56:05083982044
43345FranceAsphalt or Bitumen Tanker9603-Jan-2021 01:05:4911-Oct-2021 00:45:00083854745
44346NetherlandsShuttle Tanker913-Jan-2021 14:30:0607-Sep-2021 21:00:00083547046
45347LebanonOil or Chemical Tanker3303-Jan-2021 01:29:2801-Oct-2021 07:00:04083391247
46348HondurasOil or Chemical Tanker2311-Jan-2021 19:10:0108-Oct-2021 16:15:05083340748
47349MalaysiaOIL/CHEMICAL TANKER1805-Jan-2021 19:56:5131-May-2021 05:30:08083253049
48350MalaysiaOIL PRODUCTS TANKER609-Jan-2021 19:38:5624-Jun-2021 06:16:23083177650