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_001080

Alerts

SHIP_OWNER_NM has unique valuesUnique
DPTR_HMS has unique valuesUnique
LD_RT has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:52:08.849895
Analysis finished2023-12-10 14:52:10.404164
Duration1.55 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
2023-12-10T23:52:10.654779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length14.857143
Min length10

Characters and Unicode

Total characters728
Distinct characters48
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 rowGrXglkjgotSgxotk
2nd rowLgxKgyzZxgtyvuxz
3rd rowCotcotYnvm
4th rowMxgtjLgskYnovSmsz
5th rowYZDSgxotkYkxboik
ValueCountFrequency (%)
grxglkjgotsgxotk 1
 
2.0%
dogsktdotwosktm 1
 
2.0%
magtmjutmeajkgt 1
 
2.0%
lapogtynovvotm 1
 
2.0%
zogtpotingtmsotm 1
 
2.0%
nageagtyzgxynvm 1
 
2.0%
zgongtmynovvotm 1
 
2.0%
ynktfnktktkxme 1
 
2.0%
yngtmngomaungtm 1
 
2.0%
zogtpotjutmpogtm 1
 
2.0%
Other values (39) 39
79.6%
2023-12-10T23:52:11.152854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 101
13.9%
g 75
 
10.3%
o 73
 
10.0%
m 65
 
8.9%
n 49
 
6.7%
k 37
 
5.1%
u 33
 
4.5%
a 31
 
4.3%
Y 27
 
3.7%
v 26
 
3.6%
Other values (38) 211
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 600
82.4%
Uppercase Letter 128
 
17.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 101
16.8%
g 75
12.5%
o 73
12.2%
m 65
10.8%
n 49
8.2%
k 37
 
6.2%
u 33
 
5.5%
a 31
 
5.2%
v 26
 
4.3%
x 19
 
3.2%
Other values (16) 91
15.2%
Uppercase Letter
ValueCountFrequency (%)
Y 27
21.1%
Z 12
 
9.4%
M 10
 
7.8%
I 9
 
7.0%
N 8
 
6.2%
S 7
 
5.5%
E 6
 
4.7%
L 6
 
4.7%
J 5
 
3.9%
F 5
 
3.9%
Other values (12) 33
25.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 728
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 101
13.9%
g 75
 
10.3%
o 73
 
10.0%
m 65
 
8.9%
n 49
 
6.7%
k 37
 
5.1%
u 33
 
4.5%
a 31
 
4.3%
Y 27
 
3.7%
v 26
 
3.6%
Other values (38) 211
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 101
13.9%
g 75
 
10.3%
o 73
 
10.0%
m 65
 
8.9%
n 49
 
6.7%
k 37
 
5.1%
u 33
 
4.5%
a 31
 
4.3%
Y 27
 
3.7%
v 26
 
3.6%
Other values (38) 211
29.0%

SHIP_CNT
Real number (ℝ)

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7346939
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:52:11.300179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3163865
Coefficient of variation (CV)0.84703684
Kurtosis1.0779553
Mean2.7346939
Median Absolute Deviation (MAD)1
Skewness1.4380482
Sum134
Variance5.3656463
MonotonicityNot monotonic
2023-12-10T23:52:11.467542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 22
44.9%
2 8
 
16.3%
3 6
 
12.2%
4 5
 
10.2%
8 4
 
8.2%
5 2
 
4.1%
7 1
 
2.0%
9 1
 
2.0%
ValueCountFrequency (%)
1 22
44.9%
2 8
 
16.3%
3 6
 
12.2%
4 5
 
10.2%
5 2
 
4.1%
7 1
 
2.0%
8 4
 
8.2%
9 1
 
2.0%
ValueCountFrequency (%)
9 1
 
2.0%
8 4
 
8.2%
7 1
 
2.0%
5 2
 
4.1%
4 5
 
10.2%
3 6
 
12.2%
2 8
 
16.3%
1 22
44.9%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 00:00:09
Maximum2023-01-02 05:29:49
2023-12-10T23:52:11.636380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:11.807415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Date

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-02-08 13:52:44
Maximum2023-05-31 23:59:57
2023-12-10T23:52:11.956594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:12.111471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

LD_RT
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.20374
Minimum0.0384176
Maximum568.625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:52:12.235583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0384176
5-th percentile0.08589358
Q10.143026
median0.185275
Q30.259957
95-th percentile42.45116
Maximum568.625
Range568.58658
Interquartile range (IQR)0.116931

Descriptive statistics

Standard deviation83.763265
Coefficient of variation (CV)5.1693784
Kurtosis41.606853
Mean16.20374
Median Absolute Deviation (MAD)0.062479
Skewness6.3101821
Sum793.98328
Variance7016.2846
MonotonicityNot monotonic
2023-12-10T23:52:12.369242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0384176 1
 
2.0%
0.121326 1
 
2.0%
70.5004 1
 
2.0%
0.0789923 1
 
2.0%
0.136451 1
 
2.0%
0.17636 1
 
2.0%
0.174215 1
 
2.0%
0.213458 1
 
2.0%
0.304455 1
 
2.0%
0.163775 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0384176 1
2.0%
0.0762152 1
2.0%
0.0789923 1
2.0%
0.0962455 1
2.0%
0.102739 1
2.0%
0.109491 1
2.0%
0.115577 1
2.0%
0.115696 1
2.0%
0.120976 1
2.0%
0.121326 1
2.0%
ValueCountFrequency (%)
568.625 1
2.0%
146.084 1
2.0%
70.5004 1
2.0%
0.377301 1
2.0%
0.358497 1
2.0%
0.304455 1
2.0%
0.290718 1
2.0%
0.290411 1
2.0%
0.287524 1
2.0%
0.28649 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
2023-12-10T23:52:12.508188image/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
2023-12-10T23:52:12.682219image/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

2023-12-10T23:52:09.807615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:09.140556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:09.487170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:09.937949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:09.265391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:09.606070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:10.038515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:09.369540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:09.708088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:52:12.864672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSLD_RTRN
SHIP_OWNER_NM1.0001.0001.0001.0001.0001.000
SHIP_CNT1.0001.0001.0000.9470.8980.313
DPTR_HMS1.0001.0001.0001.0001.0001.000
ARVL_HMS1.0000.9471.0001.0001.0000.936
LD_RT1.0000.8981.0001.0001.0000.000
RN1.0000.3131.0000.9360.0001.000
2023-12-10T23:52:12.977816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTLD_RTRN
SHIP_CNT1.0000.1690.256
LD_RT0.1691.000-0.031
RN0.256-0.0311.000

Missing values

2023-12-10T23:52:10.190700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:52:10.351117image/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_HMSLD_RTRN
0GrXglkjgotSgxotk101-Jan-2023 00:00:3308-Feb-2023 13:52:440.0384182
1LgxKgyzZxgtyvuxz101-Jan-2023 00:01:4431-May-2023 23:57:520.2907183
2CotcotYnvm101-Jan-2023 04:34:0219-May-2023 05:51:040.2466734
3MxgtjLgskYnovSmsz101-Jan-2023 11:40:0701-May-2023 23:57:000.1209765
4YZDSgxotkYkxboik201-Jan-2023 00:01:0231-May-2023 23:45:49568.6256
5LMGYVkzxurIuRzj101-Jan-2023 00:12:0424-Apr-2023 03:36:030.1374087
6NgoyzgxSgxozosk101-Jan-2023 00:08:0131-May-2023 14:12:500.2875248
7IuatzxeUikgtYnvm101-Jan-2023 00:04:2431-May-2023 00:48:030.3773019
8HgnxoJxeHarq401-Jan-2023 00:00:1631-May-2023 23:59:160.29041110
9GratozkjSgxozosk101-Jan-2023 00:51:5431-May-2023 20:57:310.2864911
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSLD_RTRN
39EgtmvaFnutmdot401-Jan-2023 00:14:0431-May-2023 23:39:410.19007841
40NTGRumoyzoiy801-Jan-2023 00:00:4031-May-2023 23:57:520.17002442
41TotmhuHgutktm201-Jan-2023 00:04:1031-May-2023 23:57:560.25995743
42WotmjguSgxotk101-Jan-2023 01:11:1819-May-2023 07:32:210.15773744
43ZogtpotMautktm501-Jan-2023 00:00:4131-May-2023 23:58:550.22737345
44LapogtMaungtm101-Jan-2023 00:02:5531-May-2023 23:57:310.35849746
45ZgtmyngtNkjkYnvm101-Jan-2023 00:02:0331-May-2023 23:57:010.26283947
46TotmhuRutmynktmYn301-Jan-2023 00:02:3831-May-2023 08:21:130.14302648
47TotmhuZogtynktm301-Jan-2023 00:03:3031-May-2023 23:57:170.20176949
48TotmhuZogteoYnvm301-Jan-2023 00:00:3531-May-2023 23:59:430.10273950