Overview

Dataset statistics

Number of variables9
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory80.7 B

Variable types

Numeric4
Text1
Categorical4

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000472

Alerts

PRFMC has constant value ""Constant
FUEL_CNSMP_QTY has constant value ""Constant
RANK is highly overall correlated with RNHigh correlation
SHIP_CNT is highly overall correlated with NVGTN_DISTHigh correlation
NVGTN_DIST is highly overall correlated with SHIP_CNTHigh correlation
RN is highly overall correlated with RANKHigh correlation
DPTR_HMS is highly imbalanced (66.9%)Imbalance
RANK has unique valuesUnique
SHPYRD_NM has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:43:33.972968
Analysis finished2023-12-10 14:43:36.007496
Duration2.03 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
2023-12-10T23:43:36.086007image/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
2023-12-10T23:43:36.224059image/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%

SHPYRD_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:43:36.531171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length28
Mean length21.061224
Min length7

Characters and Unicode

Total characters1032
Distinct characters28
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

Unique49 ?
Unique (%)100.0%

Sample

1st rowHAIJIAO SHIPREPAIR & BUILDING
2nd rowBOLLINGER MARINE FABRICATORS MORGAN CITY
3rd rowFENGSHUN SHIP HEAVY INDUSTRY
4th rowZHEJIANG DONGPENG SHIPREPAIR & BUILDING
5th rowDRYDOCKS WORLD DUBAI
ValueCountFrequency (%)
shipyard 14
 
10.7%
shipbuilding 14
 
10.7%
industry 4
 
3.1%
3
 
2.3%
zhejiang 3
 
2.3%
heavy 3
 
2.3%
vard 2
 
1.5%
industries 2
 
1.5%
shiprepair 2
 
1.5%
works 2
 
1.5%
Other values (79) 82
62.6%
2023-12-10T23:43:37.466808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 118
 
11.4%
A 84
 
8.1%
82
 
7.9%
S 74
 
7.2%
N 73
 
7.1%
R 57
 
5.5%
D 53
 
5.1%
H 52
 
5.0%
E 49
 
4.7%
U 43
 
4.2%
Other values (18) 347
33.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 947
91.8%
Space Separator 82
 
7.9%
Other Punctuation 3
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 118
 
12.5%
A 84
 
8.9%
S 74
 
7.8%
N 73
 
7.7%
R 57
 
6.0%
D 53
 
5.6%
H 52
 
5.5%
E 49
 
5.2%
U 43
 
4.5%
O 40
 
4.2%
Other values (16) 304
32.1%
Space Separator
ValueCountFrequency (%)
82
100.0%
Other Punctuation
ValueCountFrequency (%)
& 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 947
91.8%
Common 85
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 118
 
12.5%
A 84
 
8.9%
S 74
 
7.8%
N 73
 
7.7%
R 57
 
6.0%
D 53
 
5.6%
H 52
 
5.5%
E 49
 
5.2%
U 43
 
4.5%
O 40
 
4.2%
Other values (16) 304
32.1%
Common
ValueCountFrequency (%)
82
96.5%
& 3
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 118
 
11.4%
A 84
 
8.1%
82
 
7.9%
S 74
 
7.2%
N 73
 
7.1%
R 57
 
5.5%
D 53
 
5.1%
H 52
 
5.0%
E 49
 
4.7%
U 43
 
4.2%
Other values (18) 347
33.6%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2244898
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:37.660279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile14.6
Maximum63
Range62
Interquartile range (IQR)3

Descriptive statistics

Standard deviation10.290095
Coefficient of variation (CV)1.9695885
Kurtosis22.463034
Mean5.2244898
Median Absolute Deviation (MAD)1
Skewness4.4878568
Sum256
Variance105.88605
MonotonicityNot monotonic
2023-12-10T23:43:37.812961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 19
38.8%
2 10
20.4%
3 5
 
10.2%
4 4
 
8.2%
9 2
 
4.1%
7 1
 
2.0%
6 1
 
2.0%
11 1
 
2.0%
10 1
 
2.0%
5 1
 
2.0%
Other values (4) 4
 
8.2%
ValueCountFrequency (%)
1 19
38.8%
2 10
20.4%
3 5
 
10.2%
4 4
 
8.2%
5 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
9 2
 
4.1%
10 1
 
2.0%
11 1
 
2.0%
ValueCountFrequency (%)
63 1
2.0%
37 1
2.0%
15 1
2.0%
14 1
2.0%
11 1
2.0%
10 1
2.0%
9 2
4.1%
7 1
2.0%
6 1
2.0%
5 1
2.0%

DPTR_HMS
Categorical

IMBALANCE 

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2021 00:00:00
42 
05-Jan-2021 00:00:00
 
1
06-Jan-2021 00:00:00
 
1
19-Jan-2021 00:00:00
 
1
21-Jan-2021 12:00:00
 
1
Other values (3)
 
3

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique7 ?
Unique (%)14.3%

Sample

1st row01-Jan-2021 00:00:00
2nd row01-Jan-2021 00:00:00
3rd row01-Jan-2021 00:00:00
4th row01-Jan-2021 00:00:00
5th row01-Jan-2021 00:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2021 00:00:00 42
85.7%
05-Jan-2021 00:00:00 1
 
2.0%
06-Jan-2021 00:00:00 1
 
2.0%
19-Jan-2021 00:00:00 1
 
2.0%
21-Jan-2021 12:00:00 1
 
2.0%
04-Feb-2021 00:00:00 1
 
2.0%
04-Jan-2021 00:00:00 1
 
2.0%
31-Mar-2021 06:00:00 1
 
2.0%

Length

2023-12-10T23:43:37.983606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:43:38.194862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 47
48.0%
01-jan-2021 42
42.9%
05-jan-2021 1
 
1.0%
06-jan-2021 1
 
1.0%
19-jan-2021 1
 
1.0%
21-jan-2021 1
 
1.0%
12:00:00 1
 
1.0%
04-feb-2021 1
 
1.0%
04-jan-2021 1
 
1.0%
31-mar-2021 1
 
1.0%

ARVL_HMS
Categorical

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
13-Oct-2021 18:00:00
30 
13-Oct-2021 12:00:00
5 
12-Oct-2021 12:00:00
 
2
11-Oct-2021 00:00:00
 
1
12-Oct-2021 18:00:00
 
1
Other values (10)
10 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique12 ?
Unique (%)24.5%

Sample

1st row13-Oct-2021 18:00:00
2nd row11-Oct-2021 00:00:00
3rd row12-Oct-2021 18:00:00
4th row13-Oct-2021 12:00:00
5th row13-Oct-2021 18:00:00

Common Values

ValueCountFrequency (%)
13-Oct-2021 18:00:00 30
61.2%
13-Oct-2021 12:00:00 5
 
10.2%
12-Oct-2021 12:00:00 2
 
4.1%
11-Oct-2021 00:00:00 1
 
2.0%
12-Oct-2021 18:00:00 1
 
2.0%
08-Jul-2021 06:00:00 1
 
2.0%
27-Aug-2021 18:00:00 1
 
2.0%
11-May-2021 12:00:00 1
 
2.0%
06-Oct-2021 18:00:00 1
 
2.0%
13-Oct-2021 06:00:00 1
 
2.0%
Other values (5) 5
 
10.2%

Length

2023-12-10T23:43:38.414613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13-oct-2021 36
36.7%
18:00:00 34
34.7%
12:00:00 10
 
10.2%
12-oct-2021 3
 
3.1%
06:00:00 3
 
3.1%
00:00:00 2
 
2.0%
11-oct-2021 1
 
1.0%
08-jul-2021 1
 
1.0%
27-aug-2021 1
 
1.0%
11-may-2021 1
 
1.0%
Other values (6) 6
 
6.1%

PRFMC
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

2023-12-10T23:43:38.600584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:43:38.736763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

FUEL_CNSMP_QTY
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

2023-12-10T23:43:38.895379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:43:39.014057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

NVGTN_DIST
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375570.47
Minimum521.122
Maximum5194230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:39.198461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum521.122
5-th percentile1124.0568
Q152463
median116441
Q3339215
95-th percentile1087100
Maximum5194230
Range5193708.9
Interquartile range (IQR)286752

Descriptive statistics

Standard deviation839963.22
Coefficient of variation (CV)2.2364997
Kurtosis24.033214
Mean375570.47
Median Absolute Deviation (MAD)101524.5
Skewness4.6167889
Sum18402953
Variance7.0553822 × 1011
MonotonicityNot monotonic
2023-12-10T23:43:39.375085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
52463.0 1
 
2.0%
840113.0 1
 
2.0%
53438.5 1
 
2.0%
227525.0 1
 
2.0%
197591.0 1
 
2.0%
2789340.0 1
 
2.0%
5194230.0 1
 
2.0%
84607.9 1
 
2.0%
70129.9 1
 
2.0%
521.122 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
521.122 1
2.0%
739.138 1
2.0%
818.028 1
2.0%
1583.1 1
2.0%
5901.46 1
2.0%
8342.26 1
2.0%
10826.3 1
2.0%
14916.5 1
2.0%
23507.2 1
2.0%
23704.4 1
2.0%
ValueCountFrequency (%)
5194230.0 1
2.0%
2789340.0 1
2.0%
1232150.0 1
2.0%
869525.0 1
2.0%
840113.0 1
2.0%
814646.0 1
2.0%
798399.0 1
2.0%
671821.0 1
2.0%
436584.0 1
2.0%
403380.0 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
2023-12-10T23:43:39.527888image/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
2023-12-10T23:43:39.647262image/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

2023-12-10T23:43:35.409193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.295878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.684122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:35.053150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:35.494792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.407249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.774142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:35.142627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:35.566056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.506270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.870644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:35.258178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:35.641798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.609964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.965569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:35.330311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:43:39.730753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSNVGTN_DISTRN
RANK1.0001.0000.0000.1780.1820.0001.000
SHPYRD_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.0001.0001.0000.0000.0000.9970.000
DPTR_HMS0.1781.0000.0001.0000.7630.0000.178
ARVL_HMS0.1821.0000.0000.7631.0000.0000.182
NVGTN_DIST0.0001.0000.9970.0000.0001.0000.000
RN1.0001.0000.0000.1780.1820.0001.000
2023-12-10T23:43:39.881587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTR_HMSARVL_HMS
DPTR_HMS1.0000.411
ARVL_HMS0.4111.000
2023-12-10T23:43:39.968029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTNVGTN_DISTRNDPTR_HMSARVL_HMS
RANK1.000-0.0220.1191.0000.0000.075
SHIP_CNT-0.0221.0000.836-0.0220.0000.000
NVGTN_DIST0.1190.8361.0000.1190.0000.000
RN1.000-0.0220.1191.0000.0000.075
DPTR_HMS0.0000.0000.0000.0001.0000.411
ARVL_HMS0.0750.0000.0000.0750.4111.000

Missing values

2023-12-10T23:43:35.780813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:43:35.923710image/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

RANKSHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
02HAIJIAO SHIPREPAIR & BUILDING101-Jan-2021 00:00:0013-Oct-2021 18:00:000052463.02
13BOLLINGER MARINE FABRICATORS MORGAN CITY101-Jan-2021 00:00:0011-Oct-2021 00:00:0000105408.03
24FENGSHUN SHIP HEAVY INDUSTRY101-Jan-2021 00:00:0012-Oct-2021 18:00:00008342.264
35ZHEJIANG DONGPENG SHIPREPAIR & BUILDING201-Jan-2021 00:00:0013-Oct-2021 12:00:00001583.15
46DRYDOCKS WORLD DUBAI101-Jan-2021 00:00:0013-Oct-2021 18:00:0000739.1386
57SHIMANAMI SHIPYARD701-Jan-2021 00:00:0008-Jul-2021 06:00:0000436584.07
68TAIZHOU WUZHOU SHIPBUILDING201-Jan-2021 00:00:0012-Oct-2021 12:00:0000155464.08
79KOCKUMS201-Jan-2021 00:00:0013-Oct-2021 18:00:0000818.0289
810NKK SHIMIZU WORKS205-Jan-2021 00:00:0027-Aug-2021 18:00:000023507.210
911EREGLI SHIPYARD201-Jan-2021 00:00:0013-Oct-2021 18:00:0000126081.011
RANKSHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
3941VARD TULCEA301-Jan-2021 00:00:0029-Jan-2021 12:00:000071103.441
4042GOTAVERKEN CITYVARVET104-Feb-2021 00:00:0013-Oct-2021 18:00:000056119.442
4143JIANGNAN SHIPBUILDING101-Jan-2021 00:00:0013-Oct-2021 18:00:0000116441.043
4244TORLAK SHIPYARD201-Jan-2021 00:00:0013-Oct-2021 18:00:0000173115.044
4345KOUAN SHIPBUILDING101-Jan-2021 00:00:0013-Oct-2021 18:00:000059357.345
4446DUZGIT YALOVA SHIPBUILDING INDUSTRY101-Jan-2021 00:00:0013-Oct-2021 18:00:000077568.546
4547SEKWANG HEAVY INDUSTRIES ULSAN1501-Jan-2021 00:00:0013-Oct-2021 18:00:0000814646.047
4648UNIVERSE SHIPBUILDING YANGZHOU104-Jan-2021 00:00:0010-Oct-2021 12:00:000038414.148
4749STERKODER SHIPBUILDING131-Mar-2021 06:00:0013-Oct-2021 18:00:000023704.449
4850LOLAND VERFT101-Jan-2021 00:00:0013-Oct-2021 12:00:000014916.550