Overview

Dataset statistics

Number of variables6
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory53.7 B

Variable types

Text1
Numeric3
Categorical2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000453

Alerts

SHIP_CNT is highly overall correlated with CDBXHigh correlation
CDBX is highly overall correlated with SHIP_CNTHigh correlation
SHIP_OWNER_NM has unique valuesUnique
CDBX has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:47:50.683987
Analysis finished2023-12-10 14:47:52.315742
Duration1.63 second
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
2023-12-10T23:47:52.497477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length15.326531
Min length3

Characters and Unicode

Total characters751
Distinct characters49
Distinct categories5 ?
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 rowGroupe Desgagnes
2nd rowCardiff Marine
3rd rowAker ASA
4th rowWest Indies Petrol
5th rowPemex
ValueCountFrequency (%)
marine 10
 
8.4%
5
 
4.2%
shpg 4
 
3.4%
petrol 2
 
1.7%
navigation 2
 
1.7%
energy 2
 
1.7%
maritime 2
 
1.7%
shipping 2
 
1.7%
mgmt 2
 
1.7%
nav 2
 
1.7%
Other values (84) 86
72.3%
2023-12-10T23:47:52.879422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 70
 
9.3%
70
 
9.3%
i 62
 
8.3%
r 56
 
7.5%
n 55
 
7.3%
a 50
 
6.7%
t 28
 
3.7%
o 27
 
3.6%
g 25
 
3.3%
s 24
 
3.2%
Other values (39) 284
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 548
73.0%
Uppercase Letter 124
 
16.5%
Space Separator 70
 
9.3%
Other Punctuation 8
 
1.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 70
12.8%
i 62
11.3%
r 56
10.2%
n 55
10.0%
a 50
 
9.1%
t 28
 
5.1%
o 27
 
4.9%
g 25
 
4.6%
s 24
 
4.4%
l 22
 
4.0%
Other values (14) 129
23.5%
Uppercase Letter
ValueCountFrequency (%)
M 23
18.5%
S 14
11.3%
P 11
8.9%
N 9
 
7.3%
T 8
 
6.5%
G 8
 
6.5%
L 7
 
5.6%
O 6
 
4.8%
A 6
 
4.8%
C 5
 
4.0%
Other values (11) 27
21.8%
Other Punctuation
ValueCountFrequency (%)
& 5
62.5%
. 3
37.5%
Space Separator
ValueCountFrequency (%)
70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 672
89.5%
Common 79
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 70
 
10.4%
i 62
 
9.2%
r 56
 
8.3%
n 55
 
8.2%
a 50
 
7.4%
t 28
 
4.2%
o 27
 
4.0%
g 25
 
3.7%
s 24
 
3.6%
M 23
 
3.4%
Other values (35) 252
37.5%
Common
ValueCountFrequency (%)
70
88.6%
& 5
 
6.3%
. 3
 
3.8%
- 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 751
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 70
 
9.3%
70
 
9.3%
i 62
 
8.3%
r 56
 
7.5%
n 55
 
7.3%
a 50
 
6.7%
t 28
 
3.7%
o 27
 
3.6%
g 25
 
3.3%
s 24
 
3.2%
Other values (39) 284
37.8%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3673469
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:53.007273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile20.6
Maximum70
Range69
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.119978
Coefficient of variation (CV)2.071783
Kurtosis24.359078
Mean5.3673469
Median Absolute Deviation (MAD)0
Skewness4.5374208
Sum263
Variance123.65391
MonotonicityNot monotonic
2023-12-10T23:47:53.114891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 31
63.3%
4 4
 
8.2%
7 2
 
4.1%
14 2
 
4.1%
5 1
 
2.0%
2 1
 
2.0%
70 1
 
2.0%
12 1
 
2.0%
13 1
 
2.0%
25 1
 
2.0%
Other values (4) 4
 
8.2%
ValueCountFrequency (%)
1 31
63.3%
2 1
 
2.0%
3 1
 
2.0%
4 4
 
8.2%
5 1
 
2.0%
6 1
 
2.0%
7 2
 
4.1%
11 1
 
2.0%
12 1
 
2.0%
13 1
 
2.0%
ValueCountFrequency (%)
70 1
2.0%
27 1
2.0%
25 1
2.0%
14 2
4.1%
13 1
2.0%
12 1
2.0%
11 1
2.0%
7 2
4.1%
6 1
2.0%
5 1
2.0%

DPTR_HMS
Categorical

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2022 12:00:00
27 
04-Jan-2022 06:00:00
3 
01-Jan-2022 18:00:00
 
2
03-Jan-2022 00:00:00
 
2
26-Apr-2022 00:00:00
 
1
Other values (14)
14 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique15 ?
Unique (%)30.6%

Sample

1st row01-Jan-2022 12:00:00
2nd row01-Jan-2022 12:00:00
3rd row03-Jan-2022 00:00:00
4th row01-Jan-2022 12:00:00
5th row01-Jan-2022 12:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2022 12:00:00 27
55.1%
04-Jan-2022 06:00:00 3
 
6.1%
01-Jan-2022 18:00:00 2
 
4.1%
03-Jan-2022 00:00:00 2
 
4.1%
26-Apr-2022 00:00:00 1
 
2.0%
08-Jan-2022 18:00:00 1
 
2.0%
16-Jan-2022 00:00:00 1
 
2.0%
05-Jan-2022 06:00:00 1
 
2.0%
15-Apr-2022 18:00:00 1
 
2.0%
11-Mar-2022 06:00:00 1
 
2.0%
Other values (9) 9
 
18.4%

Length

2023-12-10T23:47:53.242019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01-jan-2022 29
29.6%
12:00:00 28
28.6%
00:00:00 8
 
8.2%
18:00:00 7
 
7.1%
06:00:00 6
 
6.1%
04-jan-2022 5
 
5.1%
03-jan-2022 2
 
2.0%
11-jan-2022 1
 
1.0%
11-may-2022 1
 
1.0%
18-jan-2022 1
 
1.0%
Other values (10) 10
 
10.2%

ARVL_HMS
Categorical

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
17-Jul-2022 18:00:00
25 
16-Jul-2022 12:00:00
3 
17-Jul-2022 12:00:00
3 
17-Jul-2022 00:00:00
 
2
16-Jul-2022 18:00:00
 
2
Other values (14)
14 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique14 ?
Unique (%)28.6%

Sample

1st row17-Jul-2022 18:00:00
2nd row17-Jul-2022 18:00:00
3rd row17-Jul-2022 00:00:00
4th row17-Jul-2022 18:00:00
5th row17-Jul-2022 18:00:00

Common Values

ValueCountFrequency (%)
17-Jul-2022 18:00:00 25
51.0%
16-Jul-2022 12:00:00 3
 
6.1%
17-Jul-2022 12:00:00 3
 
6.1%
17-Jul-2022 00:00:00 2
 
4.1%
16-Jul-2022 18:00:00 2
 
4.1%
06-Jun-2022 18:00:00 1
 
2.0%
05-Jun-2022 00:00:00 1
 
2.0%
09-Jul-2022 06:00:00 1
 
2.0%
15-Jul-2022 00:00:00 1
 
2.0%
05-Apr-2022 18:00:00 1
 
2.0%
Other values (9) 9
 
18.4%

Length

2023-12-10T23:47:53.353746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17-jul-2022 30
30.6%
18:00:00 30
30.6%
12:00:00 9
 
9.2%
00:00:00 7
 
7.1%
16-jul-2022 6
 
6.1%
15-jul-2022 4
 
4.1%
06:00:00 3
 
3.1%
06-jun-2022 1
 
1.0%
05-jun-2022 1
 
1.0%
09-jul-2022 1
 
1.0%
Other values (6) 6
 
6.1%

CDBX
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3314016 × 1011
Minimum5.18294 × 108
Maximum4.45005 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:53.477496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.18294 × 108
5-th percentile1.3591632 × 109
Q15.97358 × 109
median3.0487 × 1010
Q31.41782 × 1011
95-th percentile7.748948 × 1011
Maximum4.45005 × 1012
Range4.4495317 × 1012
Interquartile range (IQR)1.3580842 × 1011

Descriptive statistics

Standard deviation6.6142329 × 1011
Coefficient of variation (CV)2.83702
Kurtosis35.903005
Mean2.3314016 × 1011
Median Absolute Deviation (MAD)2.738304 × 1010
Skewness5.680535
Sum1.1423868 × 1013
Variance4.3748077 × 1023
MonotonicityNot monotonic
2023-12-10T23:47:53.653321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
15221000000 1
 
2.0%
30487000000 1
 
2.0%
116660000000 1
 
2.0%
3165960000 1
 
2.0%
4148250000 1
 
2.0%
82207700000 1
 
2.0%
115714000000 1
 
2.0%
518294000 1
 
2.0%
24701700000 1
 
2.0%
5701730000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
518294000 1
2.0%
824243000 1
2.0%
988192000 1
2.0%
1915620000 1
2.0%
2218190000 1
2.0%
2638400000 1
2.0%
3103960000 1
2.0%
3165960000 1
2.0%
3298120000 1
2.0%
4148250000 1
2.0%
ValueCountFrequency (%)
4450050000000 1
2.0%
1054740000000 1
2.0%
809650000000 1
2.0%
722762000000 1
2.0%
712172000000 1
2.0%
582407000000 1
2.0%
388964000000 1
2.0%
361134000000 1
2.0%
342321000000 1
2.0%
311164000000 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
2023-12-10T23:47:53.822027image/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
2023-12-10T23:47:53.986599image/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

2023-12-10T23:47:51.856694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:50.952731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.203861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.954081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.041875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.385305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:52.066803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.116558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.454301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:47:54.103468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
SHIP_OWNER_NM1.0001.0001.0001.0001.0001.000
SHIP_CNT1.0001.0000.0000.0000.9810.217
DPTR_HMS1.0000.0001.0000.8940.0000.234
ARVL_HMS1.0000.0000.8941.0000.0000.338
CDBX1.0000.9810.0000.0001.0000.325
RN1.0000.2170.2340.3380.3251.000
2023-12-10T23:47:54.271657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ARVL_HMSDPTR_HMS
ARVL_HMS1.0000.389
DPTR_HMS0.3891.000
2023-12-10T23:47:54.365298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTCDBXRNDPTR_HMSARVL_HMS
SHIP_CNT1.0000.799-0.1290.0000.000
CDBX0.7991.0000.0400.0000.000
RN-0.1290.0401.0000.0000.086
DPTR_HMS0.0000.0000.0001.0000.389
ARVL_HMS0.0000.0000.0860.3891.000

Missing values

2023-12-10T23:47:52.169408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:47:52.275005image/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_HMSCDBXRN
0Groupe Desgagnes101-Jan-2022 12:00:0017-Jul-2022 18:00:00152210000002
1Cardiff Marine701-Jan-2022 12:00:0017-Jul-2022 18:00:003423210000003
2Aker ASA703-Jan-2022 00:00:0017-Jul-2022 00:00:003889640000004
3West Indies Petrol101-Jan-2022 12:00:0017-Jul-2022 18:00:0032981200005
4Pemex1401-Jan-2022 12:00:0017-Jul-2022 18:00:001735940000006
5Blue Marine Inca101-Jan-2022 12:00:0005-Jun-2022 00:00:0064892800007
6Blue Marine Tech101-Jan-2022 12:00:0009-Jul-2022 06:00:00106954000008
7Armamex Naviera101-Jan-2022 18:00:0017-Jul-2022 12:00:0087701700009
8TMM Grupo101-Jan-2022 12:00:0015-Jul-2022 00:00:00816937000010
9Bunkers Mexico Fuels104-Jan-2022 06:00:0005-Apr-2022 18:00:00221819000011
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
39Lynx Marine LLC101-Jan-2022 12:00:0017-Jul-2022 18:00:003944820000041
40Nissen Kaiun401-Jan-2022 12:00:0017-Jul-2022 18:00:0013868100000042
41Marine Oil Service N104-Jan-2022 06:00:0016-Jun-2022 12:00:00263840000043
42ConocoPhillips101-Jan-2022 12:00:0017-Jul-2022 18:00:0011162000000044
43Kinder Morgan601-Jan-2022 12:00:0017-Jul-2022 18:00:0031116400000045
44Seacor Holdings101-Jan-2022 12:00:0017-Jul-2022 18:00:005561310000046
45Crowley Maritime101-Jan-2022 12:00:0017-Jul-2022 18:00:003729810000047
46Genesis Energy104-Jan-2022 00:00:0017-Jul-2022 12:00:004512140000048
47Schuyler Line Nav101-Jan-2022 18:00:0017-Jul-2022 18:00:002429730000049
48Lehigh Maritime125-May-2022 12:00:0015-Jul-2022 12:00:0082424300050