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_000438

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 14:22:08.641236
Analysis finished2023-12-10 14:22:12.289015
Duration3.65 seconds
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
2023-12-10T23:22:12.391598image/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
2023-12-10T23:22:12.589442image/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

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:22:12.858857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length18
Mean length8.5102041
Min length4

Characters and Unicode

Total characters417
Distinct characters44
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

Unique34 ?
Unique (%)69.4%

Sample

1st rowGermany
2nd rowTaiwan
3rd rowNorway
4th rowEstonia
5th rowIndonesia
ValueCountFrequency (%)
norway 3
 
4.9%
netherlands 3
 
4.9%
taiwan 2
 
3.3%
south 2
 
3.3%
korea 2
 
3.3%
spain 2
 
3.3%
germany 2
 
3.3%
nigeria 2
 
3.3%
new 2
 
3.3%
republic 1
 
1.6%
Other values (40) 40
65.6%
2023-12-10T23:22:13.279423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 61
14.6%
n 39
 
9.4%
i 36
 
8.6%
e 33
 
7.9%
r 25
 
6.0%
o 21
 
5.0%
l 16
 
3.8%
u 15
 
3.6%
s 14
 
3.4%
d 14
 
3.4%
Other values (34) 143
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 342
82.0%
Uppercase Letter 62
 
14.9%
Space Separator 12
 
2.9%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 61
17.8%
n 39
11.4%
i 36
10.5%
e 33
9.6%
r 25
 
7.3%
o 21
 
6.1%
l 16
 
4.7%
u 15
 
4.4%
s 14
 
4.1%
d 14
 
4.1%
Other values (12) 68
19.9%
Uppercase Letter
ValueCountFrequency (%)
N 11
17.7%
S 10
16.1%
I 5
 
8.1%
P 5
 
8.1%
G 4
 
6.5%
C 3
 
4.8%
R 3
 
4.8%
B 3
 
4.8%
T 2
 
3.2%
U 2
 
3.2%
Other values (10) 14
22.6%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 404
96.9%
Common 13
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 61
15.1%
n 39
 
9.7%
i 36
 
8.9%
e 33
 
8.2%
r 25
 
6.2%
o 21
 
5.2%
l 16
 
4.0%
u 15
 
3.7%
s 14
 
3.5%
d 14
 
3.5%
Other values (32) 130
32.2%
Common
ValueCountFrequency (%)
12
92.3%
- 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 61
14.6%
n 39
 
9.4%
i 36
 
8.6%
e 33
 
7.9%
r 25
 
6.0%
o 21
 
5.0%
l 16
 
3.8%
u 15
 
3.6%
s 14
 
3.4%
d 14
 
3.4%
Other values (34) 143
34.3%

SHIP_KIND
Categorical

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
Crude Oil Tanker
9 
Oil Products Tanker
8 
Oil or Chemical Tanker
7 
LNG Tanker
6 
<NA>
4 
Other values (9)
15 

Length

Max length25
Median length19
Mean length15.061224
Min length4

Unique

Unique5 ?
Unique (%)10.2%

Sample

1st rowChemical Tanker
2nd row<NA>
3rd rowLNG Tanker
4th rowOil Products Tanker
5th rowOIL PRODUCTS TANKER

Common Values

ValueCountFrequency (%)
Crude Oil Tanker 9
18.4%
Oil Products Tanker 8
16.3%
Oil or Chemical Tanker 7
14.3%
LNG Tanker 6
12.2%
<NA> 4
8.2%
LPG Tanker 4
8.2%
Chemical Tanker 2
 
4.1%
Asphalt or Bitumen Tanker 2
 
4.1%
Shuttle Tanker 2
 
4.1%
OIL PRODUCTS TANKER 1
 
2.0%
Other values (4) 4
8.2%

Length

2023-12-10T23:22:13.457428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tanker 44
34.4%
oil 25
19.5%
crude 9
 
7.0%
products 9
 
7.0%
or 9
 
7.0%
chemical 9
 
7.0%
lng 6
 
4.7%
na 4
 
3.1%
lpg 4
 
3.1%
asphalt 2
 
1.6%
Other values (5) 7
 
5.5%

SHIP_CNT
Real number (ℝ)

Distinct36
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.55102
Minimum4
Maximum201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:22:13.621082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.4
Q111
median18
Q342
95-th percentile127.6
Maximum201
Range197
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.861853
Coefficient of variation (CV)1.1946907
Kurtosis4.7365394
Mean37.55102
Median Absolute Deviation (MAD)10
Skewness2.1889312
Sum1840
Variance2012.5859
MonotonicityNot monotonic
2023-12-10T23:22:13.815579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
8 3
 
6.1%
14 3
 
6.1%
13 3
 
6.1%
15 3
 
6.1%
26 2
 
4.1%
6 2
 
4.1%
4 2
 
4.1%
9 2
 
4.1%
10 2
 
4.1%
95 1
 
2.0%
Other values (26) 26
53.1%
ValueCountFrequency (%)
4 2
4.1%
5 1
 
2.0%
6 2
4.1%
8 3
6.1%
9 2
4.1%
10 2
4.1%
11 1
 
2.0%
12 1
 
2.0%
13 3
6.1%
14 3
6.1%
ValueCountFrequency (%)
201 1
2.0%
184 1
2.0%
138 1
2.0%
112 1
2.0%
106 1
2.0%
95 1
2.0%
84 1
2.0%
72 1
2.0%
69 1
2.0%
58 1
2.0%

DPTR_HMS
Date

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:00
Maximum2021-09-19 13:50:01
2023-12-10T23:22:14.042378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:14.233550image/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
Minimum2021-05-10 15:45:00
Maximum2021-10-13 23:59:05
2023-12-10T23:22:14.451313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:14.633016image/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

2023-12-10T23:22:14.800686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:14.917220image/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%
Mean953784.86
Minimum846518
Maximum1086820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:22:15.039643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum846518
5-th percentile862750.4
Q1876787
median946461
Q31012020
95-th percentile1075278
Maximum1086820
Range240302
Interquartile range (IQR)135233

Descriptive statistics

Standard deviation75753.495
Coefficient of variation (CV)0.07942409
Kurtosis-1.28154
Mean953784.86
Median Absolute Deviation (MAD)66549
Skewness0.28675979
Sum46735458
Variance5.738592 × 109
MonotonicityStrictly decreasing
2023-12-10T23:22:15.246767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1086820 1
 
2.0%
876494 1
 
2.0%
926510 1
 
2.0%
912493 1
 
2.0%
911524 1
 
2.0%
911292 1
 
2.0%
910592 1
 
2.0%
894768 1
 
2.0%
888857 1
 
2.0%
888856 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
846518 1
2.0%
853848 1
2.0%
861780 1
2.0%
864206 1
2.0%
864680 1
2.0%
868098 1
2.0%
868301 1
2.0%
870960 1
2.0%
872296 1
2.0%
874267 1
2.0%
ValueCountFrequency (%)
1086820 1
2.0%
1081170 1
2.0%
1075570 1
2.0%
1074840 1
2.0%
1074560 1
2.0%
1067320 1
2.0%
1066540 1
2.0%
1058580 1
2.0%
1030170 1
2.0%
1018080 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
2023-12-10T23:22:15.470609image/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
2023-12-10T23:22:15.637013image/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

2023-12-10T23:22:11.229948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:09.243530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:09.930675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.613050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.651269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:09.404121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.100295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.772640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.776683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:09.602199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.284496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.955628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.888220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:09.759661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.445636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.105143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:22:15.790395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKARVL_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
RANK1.0000.8440.1560.0001.0000.8240.9681.000
ARVL_CN_NM0.8441.0000.0000.8360.9950.8420.8640.844
SHIP_KIND0.1560.0001.0000.6150.6410.9780.4080.092
SHIP_CNT0.0000.8360.6151.0000.0000.9740.1540.000
DPTR_HMS1.0000.9950.6410.0001.0000.9870.9080.936
ARVL_HMS0.8240.8420.9780.9740.9871.0000.9010.768
FRGHT_CNVNC_QTY_TONM0.9680.8640.4080.1540.9080.9011.0000.961
RN1.0000.8440.0920.0000.9360.7680.9611.000
2023-12-10T23:22:15.934779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTY_TONMRNSHIP_KIND
RANK1.000-0.111-1.0001.0000.000
SHIP_CNT-0.1111.0000.111-0.1110.301
FRGHT_CNVNC_QTY_TONM-1.0000.1111.000-1.0000.145
RN1.000-0.111-1.0001.0000.000
SHIP_KIND0.0000.3010.1450.0001.000

Missing values

2023-12-10T23:22:12.044997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:22:12.226598image/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_QTY_TONMRN
0302GermanyChemical Tanker20101-Jan-2021 00:00:0013-Oct-2021 23:55:00010868202
1303Taiwan<NA>1402-Jan-2021 18:31:1618-Aug-2021 18:35:00010811703
2304NorwayLNG Tanker7201-Jan-2021 00:00:5711-Oct-2021 10:58:03010755704
3305EstoniaOil Products Tanker3804-Jan-2021 21:15:0213-Oct-2021 12:20:01010748405
4306IndonesiaOIL PRODUCTS TANKER506-Jan-2021 17:49:4124-Jun-2021 06:16:23010745606
5307SudanCrude Oil Tanker1518-Jan-2021 06:50:0614-Sep-2021 18:24:04010673207
6308New ZealandCrude Oil Tanker919-Jan-2021 10:16:3524-Aug-2021 02:39:00010665408
7309El SalvadorOil or Chemical Tanker2601-Jan-2021 01:32:3012-Oct-2021 18:50:05010585809
8310ChileLNG Tanker1914-Jan-2021 22:14:3806-Oct-2021 00:00:020103017010
9311MoroccoChemical Tanker13801-Jan-2021 00:00:0813-Oct-2021 17:00:000101808011
RANKARVL_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSCRG_TYPFRGHT_CNVNC_QTY_TONMRN
39341NetherlandsLNG Tanker4114-Jan-2021 02:30:0206-Oct-2021 08:25:01087426741
40342NorwayOil Products Tanker2113-Jan-2021 19:10:2912-Oct-2021 18:45:00087229642
41343FranceAsphalt or Bitumen Tanker9504-Jan-2021 10:00:0210-Oct-2021 12:54:02087096043
42344NorwayShuttle Tanker1013-Jan-2021 14:30:0626-Sep-2021 06:10:00086830144
43345HondurasOil or Chemical Tanker2308-Jan-2021 15:58:5605-Oct-2021 17:56:05086809845
44346SingaporeOil Products Tanker1407-Feb-2021 15:33:4910-Sep-2021 13:00:02086468046
45347ReunionOil Products Tanker607-Feb-2021 13:58:1513-Oct-2021 23:59:05086420647
46348Netherlands AntillesOil Products Tanker6904-Jan-2021 01:30:0010-Oct-2021 08:06:03086178048
47349PeruCrude Oil Tanker802-Jan-2021 20:28:5313-Oct-2021 23:53:02085384849
48350South KoreaLPG Tanker5801-Jan-2021 00:00:0511-Oct-2021 22:50:00084651850