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

Numeric4
Text1
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000443

Alerts

RANK is highly overall correlated with FRGHT_CNVNC_QTY and 1 other fieldsHigh correlation
SHIP_CNT is highly overall correlated with FRGHT_CNVNC_QTYHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with RANK and 1 other fieldsHigh correlation
RN is highly overall correlated with RANKHigh correlation
SHIP_KIND has 1 (2.0%) missing valuesMissing
FRGHT_CNVNC_QTY has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:48:21.552640
Analysis finished2023-12-10 14:48:23.515233
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RANK
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.244898
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:23.582407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q17
median15
Q327
95-th percentile36.6
Maximum39
Range38
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.612728
Coefficient of variation (CV)0.67340077
Kurtosis-1.1923827
Mean17.244898
Median Absolute Deviation (MAD)9
Skewness0.36866742
Sum845
Variance134.85544
MonotonicityNot monotonic
2023-12-10T23:48:23.719020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2 2
 
4.1%
4 2
 
4.1%
5 2
 
4.1%
6 2
 
4.1%
7 2
 
4.1%
8 2
 
4.1%
9 2
 
4.1%
10 2
 
4.1%
11 2
 
4.1%
3 2
 
4.1%
Other values (29) 29
59.2%
ValueCountFrequency (%)
1 1
2.0%
2 2
4.1%
3 2
4.1%
4 2
4.1%
5 2
4.1%
6 2
4.1%
7 2
4.1%
8 2
4.1%
9 2
4.1%
10 2
4.1%
ValueCountFrequency (%)
39 1
2.0%
38 1
2.0%
37 1
2.0%
36 1
2.0%
35 1
2.0%
34 1
2.0%
33 1
2.0%
32 1
2.0%
31 1
2.0%
30 1
2.0%

SHIP_KIND
Text

MISSING 

Distinct46
Distinct (%)95.8%
Missing1
Missing (%)2.0%
Memory size524.0 B
2023-12-10T23:48:23.945083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length25
Mean length16.583333
Min length5

Characters and Unicode

Total characters796
Distinct characters51
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

Unique44 ?
Unique (%)91.7%

Sample

1st rowTanker(crude oil)
2nd rowTanker(oil/chemical)
3rd rowTanker(chemical/oil product)
4th rowTanker(chemical)
5th rowTanker
ValueCountFrequency (%)
tanker 28
23.3%
oil 7
 
5.8%
carrier 6
 
5.0%
inland 4
 
3.3%
cargo 4
 
3.3%
or 4
 
3.3%
ship 3
 
2.5%
3
 
2.5%
bulk 3
 
2.5%
chemical 3
 
2.5%
Other values (43) 55
45.8%
2023-12-10T23:48:24.315884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
 
9.0%
r 67
 
8.4%
e 60
 
7.5%
a 59
 
7.4%
n 50
 
6.3%
T 35
 
4.4%
i 33
 
4.1%
l 30
 
3.8%
k 29
 
3.6%
o 28
 
3.5%
Other values (41) 333
41.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 479
60.2%
Uppercase Letter 224
28.1%
Space Separator 72
 
9.0%
Close Punctuation 6
 
0.8%
Open Punctuation 6
 
0.8%
Dash Punctuation 4
 
0.5%
Other Punctuation 4
 
0.5%
Decimal Number 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 67
14.0%
e 60
12.5%
a 59
12.3%
n 50
10.4%
i 33
 
6.9%
l 30
 
6.3%
k 29
 
6.1%
o 28
 
5.8%
t 17
 
3.5%
c 15
 
3.1%
Other values (14) 91
19.0%
Uppercase Letter
ValueCountFrequency (%)
T 35
15.6%
C 25
11.2%
R 20
 
8.9%
A 17
 
7.6%
E 16
 
7.1%
I 14
 
6.2%
O 12
 
5.4%
N 12
 
5.4%
L 11
 
4.9%
S 8
 
3.6%
Other values (11) 54
24.1%
Space Separator
ValueCountFrequency (%)
72
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 703
88.3%
Common 93
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 67
 
9.5%
e 60
 
8.5%
a 59
 
8.4%
n 50
 
7.1%
T 35
 
5.0%
i 33
 
4.7%
l 30
 
4.3%
k 29
 
4.1%
o 28
 
4.0%
C 25
 
3.6%
Other values (35) 287
40.8%
Common
ValueCountFrequency (%)
72
77.4%
) 6
 
6.5%
( 6
 
6.5%
- 4
 
4.3%
/ 4
 
4.3%
2 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
 
9.0%
r 67
 
8.4%
e 60
 
7.5%
a 59
 
7.4%
n 50
 
6.3%
T 35
 
4.4%
i 33
 
4.1%
l 30
 
3.8%
k 29
 
3.6%
o 28
 
3.5%
Other values (41) 333
41.8%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.44898
Minimum1
Maximum4437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:24.484958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median66
Q3370
95-th percentile2761
Maximum4437
Range4436
Interquartile range (IQR)361

Descriptive statistics

Standard deviation1002.336
Coefficient of variation (CV)1.9988793
Kurtosis7.1936258
Mean501.44898
Median Absolute Deviation (MAD)62
Skewness2.7253334
Sum24571
Variance1004677.4
MonotonicityNot monotonic
2023-12-10T23:48:24.619738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2 3
 
6.1%
7 3
 
6.1%
9 2
 
4.1%
60 2
 
4.1%
4 2
 
4.1%
5 2
 
4.1%
1728 1
 
2.0%
10 1
 
2.0%
111 1
 
2.0%
314 1
 
2.0%
Other values (31) 31
63.3%
ValueCountFrequency (%)
1 1
 
2.0%
2 3
6.1%
4 2
4.1%
5 2
4.1%
6 1
 
2.0%
7 3
6.1%
9 2
4.1%
10 1
 
2.0%
16 1
 
2.0%
28 1
 
2.0%
ValueCountFrequency (%)
4437 1
2.0%
3986 1
2.0%
2769 1
2.0%
2749 1
2.0%
1728 1
2.0%
1341 1
2.0%
1326 1
2.0%
1189 1
2.0%
596 1
2.0%
572 1
2.0%

DPTR_HMS
Date

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:00
Maximum2023-01-01 18:18:58
2023-12-10T23:48:24.745321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:24.862926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

ARVL_HMS
Date

Distinct20
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-07-03 03:47:01
Maximum2023-05-31 23:59:59
2023-12-10T23:48:24.975424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:25.097583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1942905 × 1011
Minimum23173900
Maximum1.74132 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:25.227026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23173900
5-th percentile2.605348 × 108
Q14.68861 × 109
median1.53069 × 1010
Q31.07357 × 1011
95-th percentile2.624258 × 1012
Maximum1.74132 × 1013
Range1.7413177 × 1013
Interquartile range (IQR)1.0266839 × 1011

Descriptive statistics

Standard deviation2.574318 × 1012
Coefficient of variation (CV)4.1559529
Kurtosis39.686294
Mean6.1942905 × 1011
Median Absolute Deviation (MAD)1.5034391 × 1010
Skewness6.106489
Sum3.0352024 × 1013
Variance6.6271132 × 1024
MonotonicityNot monotonic
2023-12-10T23:48:25.707528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
465969000000 1
 
2.0%
6776510000 1
 
2.0%
24152900000 1
 
2.0%
19776900000 1
 
2.0%
18172000000 1
 
2.0%
15306900000 1
 
2.0%
13716900000 1
 
2.0%
12986600000 1
 
2.0%
11946700000 1
 
2.0%
11792100000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
23173900 1
2.0%
116609000 1
2.0%
252552000 1
2.0%
272509000 1
2.0%
1683560000 1
2.0%
2712000000 1
2.0%
3512410000 1
2.0%
3639480000 1
2.0%
3734350000 1
2.0%
4151370000 1
2.0%
ValueCountFrequency (%)
17413200000000 1
2.0%
4280060000000 1
2.0%
3251050000000 1
2.0%
1684070000000 1
2.0%
1097180000000 1
2.0%
601860000000 1
2.0%
465969000000 1
2.0%
235188000000 1
2.0%
164919000000 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
2023-12-10T23:48:25.883514image/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
2023-12-10T23:48:26.065350image/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

2023-12-10T23:48:22.963728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:21.813888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:22.218747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:22.580988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:23.072086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:21.905044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:22.322838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:22.687649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:23.146683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:21.986176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:22.399245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:22.772512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:23.241889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:22.124247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:22.490158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:22.872595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:48:26.166761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
RANK1.0000.9190.0000.5490.7400.0000.957
SHIP_KIND0.9191.0000.0000.7230.9671.0000.851
SHIP_CNT0.0000.0001.0000.0000.0000.8270.389
DPTR_HMS0.5490.7230.0001.0000.9740.0000.715
ARVL_HMS0.7400.9670.0000.9741.0000.0000.835
FRGHT_CNVNC_QTY0.0001.0000.8270.0000.0001.0000.111
RN0.9570.8510.3890.7150.8350.1111.000
2023-12-10T23:48:26.303629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRN
RANK1.000-0.480-0.6320.955
SHIP_CNT-0.4801.0000.744-0.295
FRGHT_CNVNC_QTY-0.6320.7441.000-0.412
RN0.955-0.295-0.4121.000

Missing values

2023-12-10T23:48:23.355504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:48:23.472832image/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

RANKSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
02Tanker(crude oil)172801-Jan-2023 00:00:0131-May-2023 23:59:594659690000002
13Tanker(oil/chemical)118901-Jan-2023 00:00:0131-May-2023 23:59:591108250000003
24Tanker(chemical/oil product)43201-Jan-2023 00:00:0131-May-2023 23:59:59672815000004
35Tanker(chemical)32801-Jan-2023 00:00:0131-May-2023 23:59:59447401000005
46Tanker3601-Jan-2023 00:00:1231-May-2023 23:59:5937343500006
57FRUIT JUICE Tanker601-Jan-2023 00:00:2731-May-2023 23:59:4916835600007
68CO2 Tanker401-Jan-2023 00:02:4231-May-2023 23:58:022725090008
79Asphalt/Bitumen Tanker201-Jan-2023 18:18:5831-May-2023 23:05:452525520009
810Edible Oil Tanker401-Jan-2023 00:01:2131-May-2023 23:55:1511660900010
911Tanker - Hazard A (Major)201-Jan-2023 00:10:0429-May-2023 23:54:052317390011
RANKSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
3930Self Discharging Bulk Carrier201-Jan-2021 00:00:0613-Oct-2021 23:58:05558133000041
4031Bunkering Tanker33401-Jan-2021 00:00:0013-Oct-2021 23:59:05557961000042
4132Replenishment Vessel701-Jan-2021 00:00:0213-Oct-2021 23:57:02513151000043
4233Tanker - Hazard A (Major)23801-Jan-2021 00:00:0013-Oct-2021 23:59:05468861000044
4334Inland Motor Tanker37001-Jan-2021 00:00:0013-Oct-2021 23:59:05446976000045
4435Offshore Supply Ship6001-Jan-2021 00:00:0013-Oct-2021 23:59:05419335000046
4536Reefer901-Jan-2021 00:00:0413-Oct-2021 23:59:03415137000047
4637Inland Tanker6001-Jan-2021 00:00:0113-Oct-2021 23:59:04363948000048
4738Ro-Ro or Container Carrier101-Jan-2021 00:02:2613-Oct-2021 23:57:03351241000049
4839CONTAINER SHIP901-Jan-2021 00:07:4313-Oct-2021 23:59:04271200000050