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 memory70.7 B

Variable types

Numeric4
Text1
Categorical1
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001303

Alerts

SHIP_KIND has constant value ""Constant
RANK is highly overall correlated with SHIP_CNT and 2 other fieldsHigh correlation
SHIP_CNT is highly overall correlated with RANK and 2 other fieldsHigh correlation
FRGHT_CNVNC_QTY_TONM is highly overall correlated with RANK and 2 other fieldsHigh correlation
RN is highly overall correlated with RANK and 2 other fieldsHigh correlation
RANK has unique valuesUnique
ARVL_CN_NM has unique valuesUnique
ARVL_HMS has unique valuesUnique
FRGHT_CNVNC_QTY_TONM has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:30:56.046880
Analysis finished2023-12-10 14:30:58.770160
Duration2.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RANK
Real number (ℝ)

HIGH CORRELATION  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
2023-12-10T23:30:58.834081image/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
2023-12-10T23:30:58.948545image/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%

ARVL_CN_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:30:59.424068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.3877551
Min length4

Characters and Unicode

Total characters411
Distinct characters48
Distinct categories4 ?
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 rowUnited Kingdom
2nd rowGermany
3rd rowSpain
4th rowChina
5th rowIreland
ValueCountFrequency (%)
united 3
 
4.8%
new 2
 
3.2%
south 2
 
3.2%
maldives 1
 
1.6%
trinidad 1
 
1.6%
1
 
1.6%
tobago 1
 
1.6%
philippines 1
 
1.6%
latvia 1
 
1.6%
caledonia 1
 
1.6%
Other values (49) 49
77.8%
2023-12-10T23:30:59.724265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 68
16.5%
i 35
 
8.5%
n 30
 
7.3%
e 26
 
6.3%
r 20
 
4.9%
o 19
 
4.6%
l 19
 
4.6%
t 17
 
4.1%
d 15
 
3.6%
14
 
3.4%
Other values (38) 148
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 334
81.3%
Uppercase Letter 62
 
15.1%
Space Separator 14
 
3.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 68
20.4%
i 35
10.5%
n 30
9.0%
e 26
 
7.8%
r 20
 
6.0%
o 19
 
5.7%
l 19
 
5.7%
t 17
 
5.1%
d 15
 
4.5%
u 14
 
4.2%
Other values (14) 71
21.3%
Uppercase Letter
ValueCountFrequency (%)
S 8
12.9%
A 6
 
9.7%
M 5
 
8.1%
U 4
 
6.5%
P 4
 
6.5%
C 4
 
6.5%
G 4
 
6.5%
T 3
 
4.8%
L 3
 
4.8%
B 3
 
4.8%
Other values (12) 18
29.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 396
96.4%
Common 15
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 68
17.2%
i 35
 
8.8%
n 30
 
7.6%
e 26
 
6.6%
r 20
 
5.1%
o 19
 
4.8%
l 19
 
4.8%
t 17
 
4.3%
d 15
 
3.8%
u 14
 
3.5%
Other values (36) 133
33.6%
Common
ValueCountFrequency (%)
14
93.3%
& 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 68
16.5%
i 35
 
8.5%
n 30
 
7.3%
e 26
 
6.3%
r 20
 
4.9%
o 19
 
4.6%
l 19
 
4.6%
t 17
 
4.1%
d 15
 
3.6%
14
 
3.4%
Other values (38) 148
36.0%

SHIP_KIND
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
etc
49 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
etc 49
100.0%

Length

2023-12-10T23:30:59.838671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:30:59.911288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
etc 49
100.0%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.612245
Minimum5
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:30:59.989109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q110
median17
Q326
95-th percentile65.8
Maximum152
Range147
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.826747
Coefficient of variation (CV)1.0514352
Kurtosis15.047897
Mean23.612245
Median Absolute Deviation (MAD)7
Skewness3.4699603
Sum1157
Variance616.36735
MonotonicityNot monotonic
2023-12-10T23:31:00.099339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
17 4
 
8.2%
10 3
 
6.1%
11 3
 
6.1%
16 3
 
6.1%
6 3
 
6.1%
22 3
 
6.1%
21 2
 
4.1%
26 2
 
4.1%
20 2
 
4.1%
28 2
 
4.1%
Other values (17) 22
44.9%
ValueCountFrequency (%)
5 2
4.1%
6 3
6.1%
7 2
4.1%
8 2
4.1%
9 2
4.1%
10 3
6.1%
11 3
6.1%
13 1
 
2.0%
16 3
6.1%
17 4
8.2%
ValueCountFrequency (%)
152 1
2.0%
83 1
2.0%
75 1
2.0%
52 1
2.0%
51 1
2.0%
43 1
2.0%
35 1
2.0%
33 1
2.0%
29 1
2.0%
28 2
4.1%

DPTR_HMS
Date

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 00:00:07
Maximum2023-02-19 14:38:28
2023-12-10T23:31:00.202742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:31:00.302415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

ARVL_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-03-15 08:50:49
Maximum2023-05-31 23:59:26
2023-12-10T23:31:00.394281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:31:00.495221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

FRGHT_CNVNC_QTY_TONM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145611.92
Minimum40590
Maximum860672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:31:00.605919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40590
5-th percentile45034.4
Q169536
median110782
Q3159490
95-th percentile378840
Maximum860672
Range820082
Interquartile range (IQR)89954

Descriptive statistics

Standard deviation140345.23
Coefficient of variation (CV)0.96383062
Kurtosis14.342637
Mean145611.92
Median Absolute Deviation (MAD)47806
Skewness3.4065852
Sum7134984
Variance1.9696782 × 1010
MonotonicityStrictly decreasing
2023-12-10T23:31:00.711630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
860672 1
 
2.0%
63550 1
 
2.0%
102254 1
 
2.0%
99794 1
 
2.0%
98810 1
 
2.0%
93152 1
 
2.0%
86428 1
 
2.0%
86346 1
 
2.0%
81180 1
 
2.0%
79376 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
40590 1
2.0%
42968 1
2.0%
44608 1
2.0%
45674 1
2.0%
47150 1
2.0%
48708 1
2.0%
52152 1
2.0%
54448 1
2.0%
57318 1
2.0%
58630 1
2.0%
ValueCountFrequency (%)
860672 1
2.0%
512008 1
2.0%
427712 1
2.0%
305532 1
2.0%
295200 1
2.0%
253134 1
2.0%
206312 1
2.0%
190240 1
2.0%
179498 1
2.0%
166296 1
2.0%

RN
Real number (ℝ)

HIGH CORRELATION  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
2023-12-10T23:31:00.822888image/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
2023-12-10T23:31:00.936751image/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

2023-12-10T23:30:58.273141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:57.282411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:57.627904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:57.929101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:58.337693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:57.424222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:57.697143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:58.037811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:58.426005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:57.495848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:57.770061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:58.124304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:58.507692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:57.561976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:57.836935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:58.192467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:31:01.018236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKARVL_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
RANK1.0001.0000.7000.9361.0000.7181.000
ARVL_CN_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.7001.0001.0000.0001.0001.0000.700
DPTR_HMS0.9361.0000.0001.0001.0000.0000.936
ARVL_HMS1.0001.0001.0001.0001.0001.0001.000
FRGHT_CNVNC_QTY_TONM0.7181.0001.0000.0001.0001.0000.718
RN1.0001.0000.7000.9361.0000.7181.000
2023-12-10T23:31:01.106568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTY_TONMRN
RANK1.000-0.967-1.0001.000
SHIP_CNT-0.9671.0000.967-0.967
FRGHT_CNVNC_QTY_TONM-1.0000.9671.000-1.000
RN1.000-0.967-1.0001.000

Missing values

2023-12-10T23:30:58.622765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:30:58.725493image/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

RANKARVL_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
02United Kingdometc15201-Jan-2023 00:02:4531-May-2023 23:58:448606722
13Germanyetc8301-Jan-2023 00:00:2929-May-2023 20:26:405120083
24Spainetc7501-Jan-2023 00:06:2731-May-2023 23:58:494277124
35Chinaetc5101-Jan-2023 00:05:0531-May-2023 23:57:593055325
46Irelandetc5201-Jan-2023 00:03:0229-May-2023 09:09:572952006
57Portugaletc4301-Jan-2023 00:02:2629-May-2023 07:23:532531347
68Singaporeetc3501-Jan-2023 00:01:1127-May-2023 13:54:442063128
79United Statesetc2801-Jan-2023 00:05:4225-May-2023 00:52:011902409
810Belgiumetc2801-Jan-2023 03:24:2125-May-2023 04:19:2017949810
911Polandetc2908-Jan-2023 23:55:5831-May-2023 23:50:4316629611
RANKARVL_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
3941United Arab Emiratesetc901-Jan-2023 00:01:5113-May-2023 06:16:125863041
4042Saudi Arabiaetc1001-Jan-2023 00:02:4729-May-2023 21:14:535731842
4143The Bahamasetc612-Jan-2023 13:22:1831-May-2023 23:53:575444843
4244Mexicoetc601-Feb-2023 16:46:5121-May-2023 23:40:475215244
4345New Zealandetc810-Jan-2023 02:31:0029-May-2023 01:01:594870845
4446Angolaetc614-Jan-2023 08:28:0513-May-2023 00:45:384715046
4547Japanetc719-Feb-2023 14:38:2825-May-2023 22:49:424567447
4648South Africaetc514-Feb-2023 23:36:5331-May-2023 23:59:224460848
4749Qataretc707-Feb-2023 03:29:4831-May-2023 23:58:214296849
4850Ghanaetc502-Jan-2023 18:51:4415-Mar-2023 08:50:494059050