Overview

Dataset statistics

Number of variables8
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory70.7 B

Variable types

Numeric4
Text1
Categorical1
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001077

Alerts

RANK is highly overall correlated with SHIP_CNT and 3 other fieldsHigh correlation
SHIP_CNT is highly overall correlated with RANK and 2 other fieldsHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with RANK and 2 other fieldsHigh correlation
RN is highly overall correlated with RANK and 3 other fieldsHigh correlation
SHIP_KIND 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 14:18:13.424235
Analysis finished2023-12-10 14:18:16.750962
Duration3.33 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%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:18:16.858349image/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
2023-12-10T23:18:17.073303image/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%

DPTR_CN_NM
Text

Distinct37
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:18:17.413456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.9387755
Min length5

Characters and Unicode

Total characters389
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)51.0%

Sample

1st rowSouth Africa
2nd rowBrazil
3rd rowAustralia
4th rowIndia
5th rowSingapore
ValueCountFrequency (%)
united 4
 
6.7%
yemen 2
 
3.3%
mauritius 2
 
3.3%
south 2
 
3.3%
canada 2
 
3.3%
brazil 2
 
3.3%
states 2
 
3.3%
egypt 2
 
3.3%
morocco 2
 
3.3%
indonesia 2
 
3.3%
Other values (34) 38
63.3%
2023-12-10T23:18:17.924722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 58
14.9%
i 34
 
8.7%
n 33
 
8.5%
e 27
 
6.9%
r 25
 
6.4%
t 21
 
5.4%
o 18
 
4.6%
u 16
 
4.1%
s 11
 
2.8%
11
 
2.8%
Other values (32) 135
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 318
81.7%
Uppercase Letter 60
 
15.4%
Space Separator 11
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 58
18.2%
i 34
10.7%
n 33
10.4%
e 27
8.5%
r 25
 
7.9%
t 21
 
6.6%
o 18
 
5.7%
u 16
 
5.0%
s 11
 
3.5%
l 11
 
3.5%
Other values (12) 64
20.1%
Uppercase Letter
ValueCountFrequency (%)
S 9
15.0%
U 6
10.0%
M 6
10.0%
C 5
 
8.3%
I 5
 
8.3%
P 5
 
8.3%
A 4
 
6.7%
E 3
 
5.0%
B 2
 
3.3%
L 2
 
3.3%
Other values (9) 13
21.7%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 378
97.2%
Common 11
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 58
15.3%
i 34
 
9.0%
n 33
 
8.7%
e 27
 
7.1%
r 25
 
6.6%
t 21
 
5.6%
o 18
 
4.8%
u 16
 
4.2%
s 11
 
2.9%
l 11
 
2.9%
Other values (31) 124
32.8%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 389
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 58
14.9%
i 34
 
8.7%
n 33
 
8.5%
e 27
 
6.9%
r 25
 
6.4%
t 21
 
5.4%
o 18
 
4.6%
u 16
 
4.1%
s 11
 
2.8%
11
 
2.8%
Other values (32) 135
34.7%

SHIP_KIND
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Bulk Carrier
35 
BULK CARRIER
14 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBulk Carrier
2nd rowBulk Carrier
3rd rowBulk Carrier
4th rowBulk Carrier
5th rowBulk Carrier

Common Values

ValueCountFrequency (%)
Bulk Carrier 35
71.4%
BULK CARRIER 14
 
28.6%

Length

2023-12-10T23:18:18.116067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:18.255275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
bulk 49
50.0%
carrier 49
50.0%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9387755
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:18:18.409820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q15
median8
Q314
95-th percentile25.8
Maximum35
Range34
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.8909446
Coefficient of variation (CV)0.79395541
Kurtosis1.2221945
Mean9.9387755
Median Absolute Deviation (MAD)5
Skewness1.233845
Sum487
Variance62.267007
MonotonicityNot monotonic
2023-12-10T23:18:18.593246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5 8
16.3%
3 5
 
10.2%
9 4
 
8.2%
1 3
 
6.1%
2 3
 
6.1%
13 2
 
4.1%
7 2
 
4.1%
11 2
 
4.1%
17 2
 
4.1%
6 2
 
4.1%
Other values (14) 16
32.7%
ValueCountFrequency (%)
1 3
 
6.1%
2 3
 
6.1%
3 5
10.2%
4 1
 
2.0%
5 8
16.3%
6 2
 
4.1%
7 2
 
4.1%
8 2
 
4.1%
9 4
8.2%
10 1
 
2.0%
ValueCountFrequency (%)
35 1
2.0%
28 1
2.0%
27 1
2.0%
24 1
2.0%
23 1
2.0%
19 1
2.0%
18 2
4.1%
17 2
4.1%
16 1
2.0%
15 1
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:37
Maximum2022-05-25 13:44:17
2023-12-10T23:18:18.778777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:18.956880image/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
Minimum2022-01-22 11:02:28
Maximum2022-07-17 21:59:26
2023-12-10T23:18:19.152599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:19.306272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4207977 × 109
Minimum1.6289 × 108
Maximum8.77557 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:18:19.530825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6289 × 108
5-th percentile1.813664 × 108
Q12.60457 × 108
median6.71818 × 108
Q31.79471 × 109
95-th percentile5.274066 × 109
Maximum8.77557 × 109
Range8.61268 × 109
Interquartile range (IQR)1.534253 × 109

Descriptive statistics

Standard deviation1.8157809 × 109
Coefficient of variation (CV)1.278001
Kurtosis5.8740353
Mean1.4207977 × 109
Median Absolute Deviation (MAD)4.83848 × 108
Skewness2.3285631
Sum6.9619088 × 1010
Variance3.2970602 × 1018
MonotonicityStrictly decreasing
2023-12-10T23:18:19.688052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8775570000 1
 
2.0%
253583000 1
 
2.0%
512393000 1
 
2.0%
504719000 1
 
2.0%
429218000 1
 
2.0%
396351000 1
 
2.0%
395280000 1
 
2.0%
341140000 1
 
2.0%
291896000 1
 
2.0%
290260000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
162890000 1
2.0%
165047000 1
2.0%
180512000 1
2.0%
182648000 1
2.0%
186496000 1
2.0%
187970000 1
2.0%
195815000 1
2.0%
196670000 1
2.0%
219213000 1
2.0%
220140000 1
2.0%
ValueCountFrequency (%)
8775570000 1
2.0%
6590360000 1
2.0%
5274290000 1
2.0%
5273730000 1
2.0%
3637260000 1
2.0%
3588530000 1
2.0%
2829520000 1
2.0%
2769270000 1
2.0%
2268790000 1
2.0%
2066580000 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
2023-12-10T23:18:19.855613image/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
2023-12-10T23:18:20.048168image/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

2023-12-10T23:18:15.977142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:14.164417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:14.738209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:15.215744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:16.096579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:14.311036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:14.877519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:15.332233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:16.216974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:14.460720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:15.013257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:15.753509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:16.329840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:14.594862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:15.105591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:15.864226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:18:20.176021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKDPTR_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
RANK1.0000.0000.8280.1771.0000.9360.7781.000
DPTR_CN_NM0.0001.0000.0000.0001.0000.9940.7790.000
SHIP_KIND0.8280.0001.0000.0001.0001.0000.4970.828
SHIP_CNT0.1770.0000.0001.0001.0000.9010.7000.177
DPTR_HMS1.0001.0001.0001.0001.0001.0001.0001.000
ARVL_HMS0.9360.9941.0000.9011.0001.0000.0000.936
FRGHT_CNVNC_QTY0.7780.7790.4970.7001.0000.0001.0000.778
RN1.0000.0000.8280.1771.0000.9360.7781.000
2023-12-10T23:18:20.322521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRNSHIP_KIND
RANK1.000-0.634-1.0001.0000.649
SHIP_CNT-0.6341.0000.634-0.6340.000
FRGHT_CNVNC_QTY-1.0000.6341.000-1.0000.364
RN1.000-0.634-1.0001.0000.649
SHIP_KIND0.6490.0000.3640.6491.000

Missing values

2023-12-10T23:18:16.485441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:18:16.669818image/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_HMSFRGHT_CNVNC_QTYRN
02South AfricaBulk Carrier2701-Jan-2022 00:00:3717-Jul-2022 21:24:4287755700002
13BrazilBulk Carrier1801-Jan-2022 00:11:5117-Jul-2022 21:59:2265903600003
24AustraliaBulk Carrier2423-Jan-2022 03:58:5107-Jul-2022 22:57:4952742900004
35IndiaBulk Carrier1601-Jan-2022 03:06:2012-Jul-2022 22:31:0052737300005
46SingaporeBulk Carrier1419-Jan-2022 19:52:2214-Jul-2022 09:58:5736372600006
57ChinaBulk Carrier2802-Jan-2022 01:04:0117-Jul-2022 21:39:2435885300007
68IndonesiaBulk Carrier3502-Jan-2022 01:19:2807-Jul-2022 21:34:4928295200008
79CanadaBulk Carrier701-Jan-2022 15:37:1617-Jul-2022 21:59:2227692700009
810Papua New GuineaBulk Carrier1116-Jan-2022 05:19:2417-Jul-2022 21:49:15226879000010
911ArgentinaBulk Carrier1701-Jan-2022 01:03:4617-Jul-2022 21:53:34206658000011
RANKDPTR_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
3941PanamaBulk Carrier506-Jan-2022 18:09:4417-Jul-2022 21:34:3722014000041
4042YemenBULK CARRIER110-Jan-2022 03:36:0722-Jan-2022 11:02:2821921300042
4143TurkeyBulk Carrier511-Jan-2022 23:30:3910-May-2022 02:44:4519667000043
4244IndonesiaBULK CARRIER313-Jan-2022 07:32:4924-Mar-2022 22:51:5919581500044
4345United KingdomBULK CARRIER517-Jan-2022 08:07:1619-Jun-2022 02:20:3818797000045
4446PanamaBULK CARRIER302-Feb-2022 09:03:5227-May-2022 18:08:3518649600046
4547EgyptBULK CARRIER506-Jan-2022 07:45:4310-Mar-2022 20:24:5818264800047
4648MoroccoBULK CARRIER521-Jan-2022 08:20:4516-Jul-2022 20:58:0118051200048
4749DenmarkBulk Carrier703-Jan-2022 14:57:1312-Feb-2022 18:05:1816504700049
4850ChinaBULK CARRIER208-Mar-2022 15:33:0119-Apr-2022 02:56:4216289000050