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 memory71.7 B

Variable types

Text1
Categorical5
Numeric2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000466

Alerts

PRFMC has constant value ""Constant
FUEL_CNSMP_QTY has constant value ""Constant
NVGTN_DIST is highly overall correlated with SHIP_CNTHigh correlation
SHIP_CNT is highly overall correlated with NVGTN_DISTHigh correlation
SHIP_CNT is highly imbalanced (59.6%)Imbalance
DPTR_HMS is highly imbalanced (51.5%)Imbalance
SHIP_OWNER_NM has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:32:24.990771
Analysis finished2023-12-10 14:32:27.599712
Duration2.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHIP_OWNER_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:32:27.857905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length25
Mean length17.55102
Min length3

Characters and Unicode

Total characters860
Distinct characters27
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 rowFUJIAN YUANYUAN SHIPPING
2nd rowZHEJIANG CHAOSHENG SHIPPING
3rd rowDONGHAI SHIPPING
4th rowANRUN SHIPPING
5th rowHAIHUA SHIPPING
ValueCountFrequency (%)
shipping 18
 
14.8%
marine 3
 
2.5%
tankers 3
 
2.5%
shipmanagement 3
 
2.5%
maritime 2
 
1.6%
management 2
 
1.6%
trading 2
 
1.6%
nordic 2
 
1.6%
lng 2
 
1.6%
hong 2
 
1.6%
Other values (81) 83
68.0%
2023-12-10T23:32:28.278233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 96
11.2%
N 93
10.8%
A 84
 
9.8%
73
 
8.5%
E 58
 
6.7%
S 55
 
6.4%
P 50
 
5.8%
H 46
 
5.3%
G 43
 
5.0%
R 42
 
4.9%
Other values (17) 220
25.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 786
91.4%
Space Separator 73
 
8.5%
Other Punctuation 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 96
12.2%
N 93
11.8%
A 84
10.7%
E 58
 
7.4%
S 55
 
7.0%
P 50
 
6.4%
H 46
 
5.9%
G 43
 
5.5%
R 42
 
5.3%
T 32
 
4.1%
Other values (15) 187
23.8%
Space Separator
ValueCountFrequency (%)
73
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 786
91.4%
Common 74
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 96
12.2%
N 93
11.8%
A 84
10.7%
E 58
 
7.4%
S 55
 
7.0%
P 50
 
6.4%
H 46
 
5.9%
G 43
 
5.5%
R 42
 
5.3%
T 32
 
4.1%
Other values (15) 187
23.8%
Common
ValueCountFrequency (%)
73
98.6%
& 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 96
11.2%
N 93
10.8%
A 84
 
9.8%
73
 
8.5%
E 58
 
6.7%
S 55
 
6.4%
P 50
 
5.8%
H 46
 
5.3%
G 43
 
5.0%
R 42
 
4.9%
Other values (17) 220
25.6%

SHIP_CNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
1
41 
3
 
3
2
 
3
21
 
1
5
 
1

Length

Max length2
Median length1
Mean length1.0204082
Min length1

Unique

Unique2 ?
Unique (%)4.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 41
83.7%
3 3
 
6.1%
2 3
 
6.1%
21 1
 
2.0%
5 1
 
2.0%

Length

2023-12-10T23:32:28.439275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:28.552844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 41
83.7%
3 3
 
6.1%
2 3
 
6.1%
21 1
 
2.0%
5 1
 
2.0%

DPTR_HMS
Categorical

IMBALANCE 

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2021 00:00:00
36 
02-Jan-2021 00:00:00
5 
05-Jan-2021 00:00:00
 
2
10-Jan-2021 00:00:00
 
1
11-Jan-2021 00:00:00
 
1
Other values (4)
4 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique6 ?
Unique (%)12.2%

Sample

1st row01-Jan-2021 00:00:00
2nd row01-Jan-2021 00:00:00
3rd row02-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 36
73.5%
02-Jan-2021 00:00:00 5
 
10.2%
05-Jan-2021 00:00:00 2
 
4.1%
10-Jan-2021 00:00:00 1
 
2.0%
11-Jan-2021 00:00:00 1
 
2.0%
06-Jan-2021 00:00:00 1
 
2.0%
03-Feb-2021 00:00:00 1
 
2.0%
04-Jan-2021 00:00:00 1
 
2.0%
03-Jan-2021 00:00:00 1
 
2.0%

Length

2023-12-10T23:32:28.933731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:29.033522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 49
50.0%
01-jan-2021 36
36.7%
02-jan-2021 5
 
5.1%
05-jan-2021 2
 
2.0%
10-jan-2021 1
 
1.0%
11-jan-2021 1
 
1.0%
06-jan-2021 1
 
1.0%
03-feb-2021 1
 
1.0%
04-jan-2021 1
 
1.0%
03-jan-2021 1
 
1.0%

ARVL_HMS
Categorical

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
13-Oct-2021 18:00:00
22 
13-Oct-2021 12:00:00
4 
12-Oct-2021 18:00:00
 
2
13-Oct-2021 06:00:00
 
2
11-Oct-2021 18:00:00
 
2
Other values (16)
17 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique15 ?
Unique (%)30.6%

Sample

1st row12-Oct-2021 06:00:00
2nd row13-Oct-2021 18:00:00
3rd row13-Oct-2021 18:00:00
4th row12-Oct-2021 00:00:00
5th row13-Oct-2021 12:00:00

Common Values

ValueCountFrequency (%)
13-Oct-2021 18:00:00 22
44.9%
13-Oct-2021 12:00:00 4
 
8.2%
12-Oct-2021 18:00:00 2
 
4.1%
13-Oct-2021 06:00:00 2
 
4.1%
11-Oct-2021 18:00:00 2
 
4.1%
10-Oct-2021 00:00:00 2
 
4.1%
09-Jul-2021 06:00:00 1
 
2.0%
12-Oct-2021 00:00:00 1
 
2.0%
10-Jan-2021 18:00:00 1
 
2.0%
03-Jul-2021 12:00:00 1
 
2.0%
Other values (11) 11
22.4%

Length

2023-12-10T23:32:29.171225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18:00:00 29
29.6%
13-oct-2021 28
28.6%
06:00:00 9
 
9.2%
12:00:00 7
 
7.1%
12-oct-2021 4
 
4.1%
10-oct-2021 4
 
4.1%
00:00:00 4
 
4.1%
11-oct-2021 2
 
2.0%
04-aug-2021 1
 
1.0%
26-apr-2021 1
 
1.0%
Other values (9) 9
 
9.2%

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

2023-12-10T23:32:29.278975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:29.374978image/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

2023-12-10T23:32:29.467442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:29.550063image/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%
Mean112894.34
Minimum1332.14
Maximum1711330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:29.653790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1332.14
5-th percentile2204.486
Q135396.3
median61666.9
Q392328.3
95-th percentile276396.6
Maximum1711330
Range1709997.9
Interquartile range (IQR)56932

Descriptive statistics

Standard deviation244040.43
Coefficient of variation (CV)2.161671
Kurtosis40.286714
Mean112894.34
Median Absolute Deviation (MAD)26828.9
Skewness6.1071315
Sum5531822.9
Variance5.9555732 × 1010
MonotonicityNot monotonic
2023-12-10T23:32:29.784957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
39506.6 1
 
2.0%
6661.0 1
 
2.0%
193437.0 1
 
2.0%
35396.3 1
 
2.0%
36620.8 1
 
2.0%
263697.0 1
 
2.0%
64499.9 1
 
2.0%
71345.5 1
 
2.0%
124641.0 1
 
2.0%
69053.9 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1332.14 1
2.0%
1424.23 1
2.0%
1971.17 1
2.0%
2554.46 1
2.0%
3854.49 1
2.0%
6661.0 1
2.0%
10372.1 1
2.0%
25623.1 1
2.0%
30357.6 1
2.0%
30477.7 1
2.0%
ValueCountFrequency (%)
1711330.0 1
2.0%
321102.0 1
2.0%
284863.0 1
2.0%
263697.0 1
2.0%
196415.0 1
2.0%
193437.0 1
2.0%
171599.0 1
2.0%
145686.0 1
2.0%
131455.0 1
2.0%
131000.0 1
2.0%

RN
Real number (ℝ)

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
2023-12-10T23:32:29.933338image/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
2023-12-10T23:32:30.094222image/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

2023-12-10T23:32:26.990875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:26.677542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:27.135148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:26.847314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:32:30.227578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSNVGTN_DISTRN
SHIP_OWNER_NM1.0001.0001.0001.0001.0001.000
SHIP_CNT1.0001.0000.0000.5280.8900.319
DPTR_HMS1.0000.0001.0000.6650.0000.371
ARVL_HMS1.0000.5280.6651.0000.5890.000
NVGTN_DIST1.0000.8900.0000.5891.0000.021
RN1.0000.3190.3710.0000.0211.000
2023-12-10T23:32:30.339053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTARVL_HMSDPTR_HMS
SHIP_CNT1.0000.2130.000
ARVL_HMS0.2131.0000.258
DPTR_HMS0.0000.2581.000
2023-12-10T23:32:30.431269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NVGTN_DISTRNSHIP_CNTDPTR_HMSARVL_HMS
NVGTN_DIST1.0000.3670.9180.0000.268
RN0.3671.0000.1330.1930.000
SHIP_CNT0.9180.1331.0000.0000.213
DPTR_HMS0.0000.1930.0001.0000.258
ARVL_HMS0.2680.0000.2130.2581.000

Missing values

2023-12-10T23:32:27.281479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:32:27.486642image/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

SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
0FUJIAN YUANYUAN SHIPPING101-Jan-2021 00:00:0012-Oct-2021 06:00:000039506.62
1ZHEJIANG CHAOSHENG SHIPPING101-Jan-2021 00:00:0013-Oct-2021 18:00:000040730.53
2DONGHAI SHIPPING102-Jan-2021 00:00:0013-Oct-2021 18:00:000042353.74
3ANRUN SHIPPING101-Jan-2021 00:00:0012-Oct-2021 00:00:000035110.65
4HAIHUA SHIPPING101-Jan-2021 00:00:0013-Oct-2021 12:00:000046297.06
5MAERSK SHIPPING HONG KONG102-Jan-2021 00:00:0010-Jan-2021 18:00:00001424.237
6JINGHAI SHIPPING101-Jan-2021 00:00:0013-Oct-2021 12:00:00003854.498
7SINO OCEAN SHIPPING102-Jan-2021 00:00:0013-Oct-2021 18:00:000072091.79
8BAKRI NAVIGATION301-Jan-2021 00:00:0013-Oct-2021 18:00:0000321102.010
9WESTFAL LARSEN102-Jan-2021 00:00:0013-Oct-2021 18:00:000078263.011
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
39DAMICO TANKERS201-Jan-2021 00:00:0010-Aug-2021 12:00:000080889.341
40EUSU SHIPMANAGEMENT101-Jan-2021 00:00:0013-Oct-2021 18:00:0000171599.042
41DAELIM201-Jan-2021 00:00:0008-Jul-2021 18:00:000083013.043
42HANJIN SHIPMANAGEMENT103-Jan-2021 00:00:0012-Oct-2021 18:00:000063886.244
43EITZEN CHEMICAL USA101-Jan-2021 00:00:0011-Oct-2021 18:00:000061666.945
44NORDIC TANKERS TRADING501-Jan-2021 00:00:0010-Oct-2021 00:00:0000284863.046
45CS MARINE101-Jan-2021 00:00:0012-Oct-2021 18:00:0000196415.047
46DAEHO SHIPPING101-Jan-2021 00:00:0024-Sep-2021 00:00:000059713.248
47GLOBAL MARINE SERVICES201-Jan-2021 00:00:0013-Oct-2021 18:00:0000131455.049
48TOKYO LNG TANKER101-Jan-2021 00:00:0013-Oct-2021 18:00:0000123946.050