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
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000442

Alerts

SHIP_CNT is highly overall correlated with FRGHT_CNVNC_QTYHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with SHIP_CNTHigh correlation
SHIP_OWNER_NM has unique valuesUnique
RN has unique valuesUnique
FRGHT_CNVNC_QTY has 3 (6.1%) zerosZeros

Reproduction

Analysis started2023-12-10 14:57:52.567708
Analysis finished2023-12-10 14:57:57.305189
Duration4.74 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
2023-12-10T23:57:57.720818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length22
Mean length19.387755
Min length8

Characters and Unicode

Total characters950
Distinct characters26
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 rowBIG HORIZON SHIPPING
2nd rowU MING MARINE TRANSPORT SINGAP
3rd rowHAI KUO SHIPPING
4th rowST SHIPPING & TRANSPORT
5th rowMARITIME MANAGEMENT GROUP
ValueCountFrequency (%)
shipping 11
 
8.3%
tankers 6
 
4.5%
shipmanagement 4
 
3.0%
maritime 4
 
3.0%
tanker 3
 
2.3%
transport 3
 
2.3%
3
 
2.3%
management 3
 
2.3%
tsakos 2
 
1.5%
line 2
 
1.5%
Other values (83) 91
68.9%
2023-12-10T23:57:58.444125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 112
11.8%
83
 
8.7%
N 82
 
8.6%
S 79
 
8.3%
I 76
 
8.0%
E 70
 
7.4%
T 65
 
6.8%
R 60
 
6.3%
G 46
 
4.8%
P 43
 
4.5%
Other values (16) 234
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 864
90.9%
Space Separator 83
 
8.7%
Other Punctuation 3
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 112
13.0%
N 82
9.5%
S 79
9.1%
I 76
 
8.8%
E 70
 
8.1%
T 65
 
7.5%
R 60
 
6.9%
G 46
 
5.3%
P 43
 
5.0%
M 41
 
4.7%
Other values (14) 190
22.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Other Punctuation
ValueCountFrequency (%)
& 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 864
90.9%
Common 86
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 112
13.0%
N 82
9.5%
S 79
9.1%
I 76
 
8.8%
E 70
 
8.1%
T 65
 
7.5%
R 60
 
6.9%
G 46
 
5.3%
P 43
 
5.0%
M 41
 
4.7%
Other values (14) 190
22.0%
Common
ValueCountFrequency (%)
83
96.5%
& 3
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 112
11.8%
83
 
8.7%
N 82
 
8.6%
S 79
 
8.3%
I 76
 
8.0%
E 70
 
7.4%
T 65
 
6.8%
R 60
 
6.3%
G 46
 
4.8%
P 43
 
4.5%
Other values (16) 234
24.6%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0204082
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:57:58.684069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5.6
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7617439
Coefficient of variation (CV)0.87197424
Kurtosis4.5894885
Mean2.0204082
Median Absolute Deviation (MAD)0
Skewness2.1127155
Sum99
Variance3.1037415
MonotonicityNot monotonic
2023-12-10T23:57:58.977343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 30
61.2%
2 8
 
16.3%
3 3
 
6.1%
5 3
 
6.1%
4 2
 
4.1%
6 2
 
4.1%
9 1
 
2.0%
ValueCountFrequency (%)
1 30
61.2%
2 8
 
16.3%
3 3
 
6.1%
4 2
 
4.1%
5 3
 
6.1%
6 2
 
4.1%
9 1
 
2.0%
ValueCountFrequency (%)
9 1
 
2.0%
6 2
 
4.1%
5 3
 
6.1%
4 2
 
4.1%
3 3
 
6.1%
2 8
 
16.3%
1 30
61.2%

DPTR_HMS
Date

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:02
Maximum2021-03-25 23:43:28
2023-12-10T23:57:59.318191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:59.720959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

ARVL_HMS
Date

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-06-17 11:40:38
Maximum2021-10-13 23:59:05
2023-12-10T23:58:00.130030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:00.432079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8496255 × 109
Minimum0
Maximum6.58757 × 1010
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:58:00.715016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61049200
Q17.57301 × 108
median2.93185 × 109
Q38.62926 × 109
95-th percentile3.71691 × 1010
Maximum6.58757 × 1010
Range6.58757 × 1010
Interquartile range (IQR)7.871959 × 109

Descriptive statistics

Standard deviation1.3542899 × 1010
Coefficient of variation (CV)1.7252923
Kurtosis8.5649212
Mean7.8496255 × 109
Median Absolute Deviation (MAD)2.486308 × 109
Skewness2.8661779
Sum3.8463165 × 1011
Variance1.8341011 × 1020
MonotonicityNot monotonic
2023-12-10T23:58:01.007347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 3
 
6.1%
13436200000 1
 
2.0%
4921740000 1
 
2.0%
2941250000 1
 
2.0%
3026300000 1
 
2.0%
50561300000 1
 
2.0%
213703000 1
 
2.0%
445542000 1
 
2.0%
1274540000 1
 
2.0%
44380300000 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
0 3
6.1%
152623000 1
 
2.0%
213703000 1
 
2.0%
284049000 1
 
2.0%
363427000 1
 
2.0%
363603000 1
 
2.0%
444986000 1
 
2.0%
445542000 1
 
2.0%
503904000 1
 
2.0%
519097000 1
 
2.0%
ValueCountFrequency (%)
65875700000 1
2.0%
50561300000 1
2.0%
44380300000 1
2.0%
26352300000 1
2.0%
24362200000 1
2.0%
23305000000 1
2.0%
13436200000 1
2.0%
11797600000 1
2.0%
11399600000 1
2.0%
10918300000 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
2023-12-10T23:58:01.354653image/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
2023-12-10T23:58:01.682702image/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

2023-12-10T23:57:56.525033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:54.890315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.991404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.709098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.612544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.198821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.836133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.809444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.368916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:58:01.874325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
SHIP_OWNER_NM1.0001.0001.0001.0001.0001.000
SHIP_CNT1.0001.0000.9830.0000.7980.069
DPTR_HMS1.0000.9831.0000.9661.0000.841
ARVL_HMS1.0000.0000.9661.0000.0000.648
FRGHT_CNVNC_QTY1.0000.7981.0000.0001.0000.331
RN1.0000.0690.8410.6480.3311.000
2023-12-10T23:58:02.056400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTFRGHT_CNVNC_QTYRN
SHIP_CNT1.0000.5150.133
FRGHT_CNVNC_QTY0.5151.0000.127
RN0.1330.1271.000

Missing values

2023-12-10T23:57:57.021363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:57:57.223916image/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_HMSFRGHT_CNVNC_QTYRN
0BIG HORIZON SHIPPING101-Jan-2021 04:49:2413-Oct-2021 21:01:05134362000002
1U MING MARINE TRANSPORT SINGAP101-Jan-2021 00:03:5608-Sep-2021 02:25:05101479000003
2HAI KUO SHIPPING201-Jan-2021 00:00:0213-Oct-2021 23:49:0120726600004
3ST SHIPPING & TRANSPORT901-Jan-2021 00:00:0513-Oct-2021 23:57:03263523000005
4MARITIME MANAGEMENT GROUP101-Jan-2021 00:02:4513-Oct-2021 20:58:008981260006
5ZAKYNTHOS SHIPPING101-Jan-2021 00:11:2913-Oct-2021 21:18:03113996000007
6NYK STOLT SHIPHOLDING301-Jan-2021 00:02:2513-Oct-2021 23:58:0011864300008
7SHELL TANKERS AUSTRALIA101-Jan-2021 00:03:2713-Oct-2021 23:41:0290774300009
8ORIENT ENTERPRISE101-Jan-2021 05:05:5608-Jul-2021 05:32:14010
9TRADA INTERNATIONAL101-Jan-2021 00:01:2613-Oct-2021 22:40:04128439000011
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
39QATARGAS601-Jan-2021 00:51:3313-Oct-2021 23:58:056587570000041
40CAPITAL SHIPMANAGEMENT101-Jan-2021 00:00:1613-Oct-2021 23:58:04274201000042
41HANJIN OVERSEAS TANKER301-Jan-2021 00:00:3713-Oct-2021 23:55:00210388000043
42SONGA SHIPPING401-Jan-2021 00:01:3913-Oct-2021 23:59:01293185000044
43NOVA TANKERS301-Jan-2021 00:00:2913-Oct-2021 23:58:011179760000045
44SIGMA TANKERS101-Jan-2021 01:38:1213-Oct-2021 23:58:05371579000046
45TROY SHIPPING & TOURISM101-Jan-2021 00:00:0713-Oct-2021 23:58:00364042000047
46SEMUA SHIPPING201-Jan-2021 00:00:1013-Oct-2021 23:59:00100509000048
47TSAKOS COLUMBIA SHIPMANAGEMENT201-Jan-2021 00:35:5313-Oct-2021 23:59:051091830000049
48MANSOURIA TANKERS101-Jan-2021 00:24:1713-Oct-2021 23:56:01273589000050