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_001318

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 14:43:22.617186
Analysis finished2023-12-10 14:43:26.255788
Duration3.64 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
2023-12-10T23:43:26.457892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length12.673469
Min length2

Characters and Unicode

Total characters621
Distinct characters42
Distinct categories2 ?
Distinct scripts1 ?
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 rowQD
2nd rowGhosklv
3rd rowQruglfKdpexuj
4th rowFdshVklsslqj
5th rowFrqwvklsvPdqdjhphqw
ValueCountFrequency (%)
qd 1
 
2.0%
pvf 1
 
2.0%
kvvfkliidkuwv 1
 
2.0%
hdvwhuqphg 1
 
2.0%
uhhghuhlqrug 1
 
2.0%
eruhdolvpdulwlph 1
 
2.0%
kdsdjoorbgfrqw 1
 
2.0%
yvklsvkdpexuj 1
 
2.0%
uhhghuhludperz 1
 
2.0%
qruwkhuqvklsslqj 1
 
2.0%
Other values (39) 39
79.6%
2023-12-10T23:43:26.850387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 72
 
11.6%
l 55
 
8.9%
d 44
 
7.1%
u 41
 
6.6%
v 34
 
5.5%
s 32
 
5.2%
r 29
 
4.7%
q 29
 
4.7%
w 24
 
3.9%
k 23
 
3.7%
Other values (32) 238
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 496
79.9%
Uppercase Letter 125
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 72
14.5%
l 55
11.1%
d 44
 
8.9%
u 41
 
8.3%
v 34
 
6.9%
s 32
 
6.5%
r 29
 
5.8%
q 29
 
5.8%
w 24
 
4.8%
k 23
 
4.6%
Other values (12) 113
22.8%
Uppercase Letter
ValueCountFrequency (%)
V 21
16.8%
F 20
16.0%
O 12
9.6%
P 10
8.0%
U 9
 
7.2%
Q 8
 
6.4%
D 6
 
4.8%
S 6
 
4.8%
E 5
 
4.0%
K 4
 
3.2%
Other values (10) 24
19.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 621
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 72
 
11.6%
l 55
 
8.9%
d 44
 
7.1%
u 41
 
6.6%
v 34
 
5.5%
s 32
 
5.2%
r 29
 
4.7%
q 29
 
4.7%
w 24
 
3.9%
k 23
 
3.7%
Other values (32) 238
38.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 621
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 72
 
11.6%
l 55
 
8.9%
d 44
 
7.1%
u 41
 
6.6%
v 34
 
5.5%
s 32
 
5.2%
r 29
 
4.7%
q 29
 
4.7%
w 24
 
3.9%
k 23
 
3.7%
Other values (32) 238
38.3%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.571429
Minimum1
Maximum1104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:27.006423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q332
95-th percentile112.2
Maximum1104
Range1103
Interquartile range (IQR)27

Descriptive statistics

Standard deviation161.9299
Coefficient of variation (CV)3.3338508
Kurtosis39.626442
Mean48.571429
Median Absolute Deviation (MAD)9
Skewness6.1172254
Sum2380
Variance26221.292
MonotonicityNot monotonic
2023-12-10T23:43:27.128773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 5
 
10.2%
9 4
 
8.2%
1 4
 
8.2%
7 3
 
6.1%
8 2
 
4.1%
13 2
 
4.1%
10 2
 
4.1%
32 2
 
4.1%
36 2
 
4.1%
4 2
 
4.1%
Other values (21) 21
42.9%
ValueCountFrequency (%)
1 4
8.2%
2 5
10.2%
3 1
 
2.0%
4 2
 
4.1%
5 1
 
2.0%
7 3
6.1%
8 2
 
4.1%
9 4
8.2%
10 2
 
4.1%
11 1
 
2.0%
ValueCountFrequency (%)
1104 1
2.0%
329 1
2.0%
113 1
2.0%
111 1
2.0%
61 1
2.0%
54 1
2.0%
42 1
2.0%
41 1
2.0%
39 1
2.0%
36 2
4.1%

DPTR_HMS
Date

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 06:00:00
Maximum2023-05-06 00:00:00
2023-12-10T23:43:27.318450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.435733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

ARVL_HMS
Date

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-28 18:00:00
Maximum2023-05-31 18:00:00
2023-12-10T23:43:27.557175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.662723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

CDBX
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9187809 × 1011
Minimum3.83395 × 109
Maximum4.81892 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:27.815304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.83395 × 109
5-th percentile5.23409 × 109
Q12.66034 × 1010
median9.02945 × 1010
Q32.15225 × 1011
95-th percentile1.580324 × 1012
Maximum4.81892 × 1012
Range4.815086 × 1012
Interquartile range (IQR)1.886216 × 1011

Descriptive statistics

Standard deviation8.8860946 × 1011
Coefficient of variation (CV)2.2675661
Kurtosis16.103494
Mean3.9187809 × 1011
Median Absolute Deviation (MAD)7.98326 × 1010
Skewness3.8841964
Sum1.9202026 × 1013
Variance7.8962677 × 1023
MonotonicityNot monotonic
2023-12-10T23:43:27.963670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4818920000000 1
 
2.0%
433231000000 1
 
2.0%
28350500000 1
 
2.0%
64645100000 1
 
2.0%
90294500000 1
 
2.0%
175203000000 1
 
2.0%
1615400000000 1
 
2.0%
55925500000 1
 
2.0%
11366800000 1
 
2.0%
367577000000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3833950000 1
2.0%
4106380000 1
2.0%
4341210000 1
2.0%
6573410000 1
2.0%
6888940000 1
2.0%
7538510000 1
2.0%
8848290000 1
2.0%
10461900000 1
2.0%
11366800000 1
2.0%
17704000000 1
2.0%
ValueCountFrequency (%)
4818920000000 1
2.0%
3709530000000 1
2.0%
1615400000000 1
2.0%
1527710000000 1
2.0%
878920000000 1
2.0%
818252000000 1
2.0%
707678000000 1
2.0%
626965000000 1
2.0%
495635000000 1
2.0%
495141000000 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
2023-12-10T23:43:28.099216image/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
2023-12-10T23:43:28.228384image/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

2023-12-10T23:43:25.730443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:24.971530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.453771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.818076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.214864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.545875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.898015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.349783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.640242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:43:28.341235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
SHIP_OWNER_NM1.0001.0001.0001.0001.0001.000
SHIP_CNT1.0001.0000.0000.0000.9000.000
DPTR_HMS1.0000.0001.0000.4970.0000.325
ARVL_HMS1.0000.0000.4971.0000.0000.293
CDBX1.0000.9000.0000.0001.0000.621
RN1.0000.0000.3250.2930.6211.000
2023-12-10T23:43:28.455312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTCDBXRN
SHIP_CNT1.0000.9570.076
CDBX0.9571.0000.157
RN0.0760.1571.000

Missing values

2023-12-10T23:43:26.017721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:43:26.177984image/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
0QD110401-Jan-2023 12:00:0031-May-2023 18:00:0048189200000002
1Ghosklv1601-Jan-2023 12:00:0023-May-2023 06:00:001232950000003
2QruglfKdpexuj1301-Jan-2023 12:00:0031-May-2023 18:00:00515700000004
3FdshVklsslqj1001-Jan-2023 12:00:0031-May-2023 18:00:00880937000005
4FrqwvklsvPdqdjhphqw3101-Jan-2023 12:00:0031-May-2023 18:00:001208530000006
5PduorzQdyljdwlrq1301-Jan-2023 12:00:0031-May-2023 18:00:00821860000007
6MxqjhukdqvFr921-Apr-2023 18:00:0031-May-2023 18:00:00363032000008
7GdqdrvVklsslqj5401-Jan-2023 12:00:0031-May-2023 18:00:008182520000009
8HoeghlfkUhhghuhl901-Jan-2023 12:00:0031-May-2023 18:00:003353630000010
9RfhdqlfPdulwlphOwg101-Jan-2023 12:00:0031-May-2023 18:00:00410638000011
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
39ZhvvhovUhhghuhl201-Jan-2023 12:00:0031-May-2023 18:00:001046190000041
40DQO201-Jan-2023 12:00:0031-May-2023 18:00:002534570000042
41FVVFVksjOhdvlqj501-Jan-2023 12:00:0031-May-2023 18:00:008967610000043
42DundvVklsslqj4201-Jan-2023 12:00:0031-May-2023 18:00:0021522500000044
43FdslwdoSurgxfw1201-Jan-2023 12:00:0031-May-2023 00:00:0019422400000045
44DSO3601-Jan-2023 12:00:0031-May-2023 06:00:0070767800000046
45FrvwdpduhVklsslqj6101-Apr-2023 12:00:0031-May-2023 18:00:0087892000000047
46UhhghuhlFSRiihq3301-Jan-2023 12:00:0031-May-2023 18:00:0062696500000048
47JoredoVklsOhdvh3902-Jan-2023 18:00:0030-May-2023 12:00:0049514100000049
48OhswdVklsslqj401-Jan-2023 12:00:0031-May-2023 18:00:002624680000050