Overview

Dataset statistics

Number of variables11
Number of observations49
Missing cells39
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory93.7 B

Variable types

Text6
Numeric3
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001098

Alerts

CDBX is highly overall correlated with RNHigh correlation
RN is highly overall correlated with CDBXHigh correlation
DPRT_PRT_CD has 23 (46.9%) missing valuesMissing
ARRV_PRT_CD has 16 (32.7%) missing valuesMissing
DPTR_HMS has unique valuesUnique
ARVL_HMS has unique valuesUnique
CDBX has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:48:56.068315
Analysis finished2023-12-10 14:48:57.667417
Duration1.6 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DPTR_CN_NM
Text

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:48:57.806994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length16
Mean length7.7755102
Min length4

Characters and Unicode

Total characters381
Distinct characters47
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

Unique25 ?
Unique (%)51.0%

Sample

1st rowPuerto Rico
2nd rowChina
3rd rowPapua New Guinea
4th rowFrance
5th rowNetherlands
ValueCountFrequency (%)
spain 4
 
6.2%
france 3
 
4.7%
china 3
 
4.7%
south 3
 
4.7%
new 3
 
4.7%
taiwan 2
 
3.1%
malta 2
 
3.1%
indonesia 2
 
3.1%
india 2
 
3.1%
guinea 2
 
3.1%
Other values (35) 38
59.4%
2023-12-10T23:48:58.132731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 57
15.0%
i 33
 
8.7%
n 30
 
7.9%
e 30
 
7.9%
o 21
 
5.5%
r 18
 
4.7%
15
 
3.9%
t 13
 
3.4%
u 12
 
3.1%
l 11
 
2.9%
Other values (37) 141
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 300
78.7%
Uppercase Letter 64
 
16.8%
Space Separator 15
 
3.9%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 57
19.0%
i 33
11.0%
n 30
10.0%
e 30
10.0%
o 21
 
7.0%
r 18
 
6.0%
t 13
 
4.3%
u 12
 
4.0%
l 11
 
3.7%
c 10
 
3.3%
Other values (14) 65
21.7%
Uppercase Letter
ValueCountFrequency (%)
S 10
15.6%
I 7
10.9%
C 7
10.9%
M 5
 
7.8%
P 4
 
6.2%
N 4
 
6.2%
T 4
 
6.2%
K 3
 
4.7%
F 3
 
4.7%
A 3
 
4.7%
Other values (10) 14
21.9%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 364
95.5%
Common 17
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 57
15.7%
i 33
 
9.1%
n 30
 
8.2%
e 30
 
8.2%
o 21
 
5.8%
r 18
 
4.9%
t 13
 
3.6%
u 12
 
3.3%
l 11
 
3.0%
S 10
 
2.7%
Other values (34) 129
35.4%
Common
ValueCountFrequency (%)
15
88.2%
) 1
 
5.9%
( 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 57
15.0%
i 33
 
8.7%
n 30
 
7.9%
e 30
 
7.9%
o 21
 
5.5%
r 18
 
4.7%
15
 
3.9%
t 13
 
3.4%
u 12
 
3.1%
l 11
 
2.9%
Other values (37) 141
37.0%

ARVL_CN_NM
Text

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:48:58.306608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.6530612
Min length4

Characters and Unicode

Total characters375
Distinct characters38
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

Unique20 ?
Unique (%)40.8%

Sample

1st rowColombia
2nd rowAustralia
3rd rowJapan
4th rowSpain
5th rowDenmark
ValueCountFrequency (%)
australia 5
 
8.6%
china 4
 
6.9%
united 4
 
6.9%
spain 4
 
6.9%
states 3
 
5.2%
malaysia 3
 
5.2%
turkey 2
 
3.4%
uruguay 2
 
3.4%
indonesia 2
 
3.4%
ecuador 2
 
3.4%
Other values (26) 27
46.6%
2023-12-10T23:48:58.605942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 66
17.6%
i 30
 
8.0%
n 27
 
7.2%
r 26
 
6.9%
e 25
 
6.7%
t 23
 
6.1%
s 17
 
4.5%
u 17
 
4.5%
l 13
 
3.5%
d 12
 
3.2%
Other values (28) 119
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 308
82.1%
Uppercase Letter 58
 
15.5%
Space Separator 9
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 66
21.4%
i 30
9.7%
n 27
8.8%
r 26
 
8.4%
e 25
 
8.1%
t 23
 
7.5%
s 17
 
5.5%
u 17
 
5.5%
l 13
 
4.2%
d 12
 
3.9%
Other values (10) 52
16.9%
Uppercase Letter
ValueCountFrequency (%)
S 9
15.5%
C 7
12.1%
I 6
10.3%
A 6
10.3%
U 6
10.3%
E 5
8.6%
M 4
6.9%
N 3
 
5.2%
G 2
 
3.4%
T 2
 
3.4%
Other values (7) 8
13.8%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 366
97.6%
Common 9
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 66
18.0%
i 30
 
8.2%
n 27
 
7.4%
r 26
 
7.1%
e 25
 
6.8%
t 23
 
6.3%
s 17
 
4.6%
u 17
 
4.6%
l 13
 
3.6%
d 12
 
3.3%
Other values (27) 110
30.1%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 66
17.6%
i 30
 
8.0%
n 27
 
7.2%
r 26
 
6.9%
e 25
 
6.7%
t 23
 
6.1%
s 17
 
4.5%
u 17
 
4.5%
l 13
 
3.5%
d 12
 
3.2%
Other values (28) 119
31.7%

DPRT_PRT_CD
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing23
Missing (%)46.9%
Memory size524.0 B
2023-12-10T23:48:58.776749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters130
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st rowCNTXG
2nd rowESLPA
3rd rowGRMLO
4th rowBRPOU
5th rowPKBQM
ValueCountFrequency (%)
cntxg 1
 
3.8%
eslpa 1
 
3.8%
rukan 1
 
3.8%
clanf 1
 
3.8%
cobun 1
 
3.8%
frsnr 1
 
3.8%
jptky 1
 
3.8%
trcey 1
 
3.8%
cnynt 1
 
3.8%
frcqf 1
 
3.8%
Other values (16) 16
61.5%
2023-12-10T23:48:59.048192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 13
 
10.0%
A 11
 
8.5%
P 10
 
7.7%
R 10
 
7.7%
K 8
 
6.2%
C 7
 
5.4%
M 7
 
5.4%
O 6
 
4.6%
L 5
 
3.8%
T 5
 
3.8%
Other values (15) 48
36.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 130
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 13
 
10.0%
A 11
 
8.5%
P 10
 
7.7%
R 10
 
7.7%
K 8
 
6.2%
C 7
 
5.4%
M 7
 
5.4%
O 6
 
4.6%
L 5
 
3.8%
T 5
 
3.8%
Other values (15) 48
36.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 130
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 13
 
10.0%
A 11
 
8.5%
P 10
 
7.7%
R 10
 
7.7%
K 8
 
6.2%
C 7
 
5.4%
M 7
 
5.4%
O 6
 
4.6%
L 5
 
3.8%
T 5
 
3.8%
Other values (15) 48
36.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 13
 
10.0%
A 11
 
8.5%
P 10
 
7.7%
R 10
 
7.7%
K 8
 
6.2%
C 7
 
5.4%
M 7
 
5.4%
O 6
 
4.6%
L 5
 
3.8%
T 5
 
3.8%
Other values (15) 48
36.9%

ARRV_PRT_CD
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing16
Missing (%)32.7%
Memory size524.0 B
2023-12-10T23:48:59.219799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters165
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st rowCOCTG
2nd rowAUPHE
3rd rowJPOWA
4th rowESFRO
5th rowDKAAL
ValueCountFrequency (%)
cnzos 1
 
3.0%
inixz 1
 
3.0%
cachs 1
 
3.0%
cnzuh 1
 
3.0%
itran 1
 
3.0%
erasa 1
 
3.0%
nldhr 1
 
3.0%
auwei 1
 
3.0%
mysdk 1
 
3.0%
trayt 1
 
3.0%
Other values (23) 23
69.7%
2023-12-10T23:48:59.504912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 16
 
9.7%
S 12
 
7.3%
E 12
 
7.3%
C 12
 
7.3%
R 11
 
6.7%
U 11
 
6.7%
N 10
 
6.1%
T 9
 
5.5%
I 9
 
5.5%
H 7
 
4.2%
Other values (15) 56
33.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 165
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 16
 
9.7%
S 12
 
7.3%
E 12
 
7.3%
C 12
 
7.3%
R 11
 
6.7%
U 11
 
6.7%
N 10
 
6.1%
T 9
 
5.5%
I 9
 
5.5%
H 7
 
4.2%
Other values (15) 56
33.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 165
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 16
 
9.7%
S 12
 
7.3%
E 12
 
7.3%
C 12
 
7.3%
R 11
 
6.7%
U 11
 
6.7%
N 10
 
6.1%
T 9
 
5.5%
I 9
 
5.5%
H 7
 
4.2%
Other values (15) 56
33.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 16
 
9.7%
S 12
 
7.3%
E 12
 
7.3%
C 12
 
7.3%
R 11
 
6.7%
U 11
 
6.7%
N 10
 
6.1%
T 9
 
5.5%
I 9
 
5.5%
H 7
 
4.2%
Other values (15) 56
33.9%

DPTR_PRT_NM
Text

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:48:59.732870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length21
Mean length14.816327
Min length5

Characters and Unicode

Total characters726
Distinct characters57
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)91.8%

Sample

1st rowMona Passage West
2nd rowTianjin Xingang
3rd rowJomard Passage
4th rowSeine Bay
5th rowTerschellinger Bank - NE Bound
ValueCountFrequency (%)
8
 
6.7%
bound 7
 
5.9%
offshore 5
 
4.2%
gibraltar 4
 
3.4%
terminal 4
 
3.4%
port 3
 
2.5%
no.2 3
 
2.5%
ta 3
 
2.5%
passage 3
 
2.5%
tsa 2
 
1.7%
Other values (72) 77
64.7%
2023-12-10T23:49:00.357153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 89
 
12.3%
70
 
9.6%
n 52
 
7.2%
o 44
 
6.1%
r 43
 
5.9%
e 41
 
5.6%
s 35
 
4.8%
i 33
 
4.5%
l 22
 
3.0%
t 20
 
2.8%
Other values (47) 277
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 521
71.8%
Uppercase Letter 111
 
15.3%
Space Separator 70
 
9.6%
Dash Punctuation 8
 
1.1%
Other Punctuation 5
 
0.7%
Close Punctuation 4
 
0.6%
Open Punctuation 4
 
0.6%
Decimal Number 3
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 89
17.1%
n 52
10.0%
o 44
 
8.4%
r 43
 
8.3%
e 41
 
7.9%
s 35
 
6.7%
i 33
 
6.3%
l 22
 
4.2%
t 20
 
3.8%
u 19
 
3.6%
Other values (15) 123
23.6%
Uppercase Letter
ValueCountFrequency (%)
T 15
13.5%
P 13
11.7%
A 8
 
7.2%
E 7
 
6.3%
B 7
 
6.3%
N 7
 
6.3%
M 6
 
5.4%
G 6
 
5.4%
O 6
 
5.4%
S 5
 
4.5%
Other values (15) 31
27.9%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
, 2
40.0%
Space Separator
ValueCountFrequency (%)
70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Decimal Number
ValueCountFrequency (%)
2 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 632
87.1%
Common 94
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 89
 
14.1%
n 52
 
8.2%
o 44
 
7.0%
r 43
 
6.8%
e 41
 
6.5%
s 35
 
5.5%
i 33
 
5.2%
l 22
 
3.5%
t 20
 
3.2%
u 19
 
3.0%
Other values (40) 234
37.0%
Common
ValueCountFrequency (%)
70
74.5%
- 8
 
8.5%
) 4
 
4.3%
( 4
 
4.3%
. 3
 
3.2%
2 3
 
3.2%
, 2
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 89
 
12.3%
70
 
9.6%
n 52
 
7.2%
o 44
 
6.1%
r 43
 
5.9%
e 41
 
5.6%
s 35
 
4.8%
i 33
 
4.5%
l 22
 
3.0%
t 20
 
2.8%
Other values (47) 277
38.2%

ARVL_PRT_NM
Text

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:49:00.595027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length25
Mean length15.693878
Min length4

Characters and Unicode

Total characters769
Distinct characters57
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)95.9%

Sample

1st rowCartagena, Colombia
2nd rowPort Hedland
3rd rowOwase
4th rowFerrol
5th rowAalborg
ValueCountFrequency (%)
offshore 7
 
5.9%
ta 4
 
3.4%
no.2 3
 
2.5%
tsa 3
 
2.5%
3
 
2.5%
cape 3
 
2.5%
south 2
 
1.7%
west 2
 
1.7%
port 2
 
1.7%
singapore 2
 
1.7%
Other values (86) 88
73.9%
2023-12-10T23:49:00.928977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 88
 
11.4%
70
 
9.1%
o 57
 
7.4%
e 50
 
6.5%
n 46
 
6.0%
r 44
 
5.7%
s 28
 
3.6%
t 27
 
3.5%
l 27
 
3.5%
h 26
 
3.4%
Other values (47) 306
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 525
68.3%
Uppercase Letter 132
 
17.2%
Space Separator 70
 
9.1%
Other Punctuation 19
 
2.5%
Open Punctuation 6
 
0.8%
Close Punctuation 6
 
0.8%
Decimal Number 6
 
0.8%
Dash Punctuation 5
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 88
16.8%
o 57
10.9%
e 50
9.5%
n 46
8.8%
r 44
8.4%
s 28
 
5.3%
t 27
 
5.1%
l 27
 
5.1%
h 26
 
5.0%
i 25
 
4.8%
Other values (15) 107
20.4%
Uppercase Letter
ValueCountFrequency (%)
S 17
12.9%
A 14
10.6%
T 12
9.1%
P 12
9.1%
C 11
 
8.3%
N 10
 
7.6%
O 9
 
6.8%
B 8
 
6.1%
R 5
 
3.8%
M 5
 
3.8%
Other values (13) 29
22.0%
Other Punctuation
ValueCountFrequency (%)
. 12
63.2%
, 6
31.6%
; 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
2 3
50.0%
Space Separator
ValueCountFrequency (%)
70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 657
85.4%
Common 112
 
14.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 88
 
13.4%
o 57
 
8.7%
e 50
 
7.6%
n 46
 
7.0%
r 44
 
6.7%
s 28
 
4.3%
t 27
 
4.1%
l 27
 
4.1%
h 26
 
4.0%
i 25
 
3.8%
Other values (38) 239
36.4%
Common
ValueCountFrequency (%)
70
62.5%
. 12
 
10.7%
, 6
 
5.4%
( 6
 
5.4%
) 6
 
5.4%
- 5
 
4.5%
1 3
 
2.7%
2 3
 
2.7%
; 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 769
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 88
 
11.4%
70
 
9.1%
o 57
 
7.4%
e 50
 
6.5%
n 46
 
6.0%
r 44
 
5.7%
s 28
 
3.6%
t 27
 
3.5%
l 27
 
3.5%
h 26
 
3.4%
Other values (47) 306
39.8%

SHIP_CNT
Real number (ℝ)

Distinct17
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.959184
Minimum3
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:01.037026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5.4
Q19
median11
Q313
95-th percentile16.6
Maximum22
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6683283
Coefficient of variation (CV)0.33472642
Kurtosis1.6587398
Mean10.959184
Median Absolute Deviation (MAD)2
Skewness0.62792155
Sum537
Variance13.456633
MonotonicityNot monotonic
2023-12-10T23:49:01.142750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11 7
14.3%
12 7
14.3%
9 7
14.3%
14 5
10.2%
10 5
10.2%
8 4
8.2%
13 3
6.1%
6 2
 
4.1%
5 1
 
2.0%
7 1
 
2.0%
Other values (7) 7
14.3%
ValueCountFrequency (%)
3 1
 
2.0%
4 1
 
2.0%
5 1
 
2.0%
6 2
 
4.1%
7 1
 
2.0%
8 4
8.2%
9 7
14.3%
10 5
10.2%
11 7
14.3%
12 7
14.3%
ValueCountFrequency (%)
22 1
 
2.0%
21 1
 
2.0%
17 1
 
2.0%
16 1
 
2.0%
15 1
 
2.0%
14 5
10.2%
13 3
6.1%
12 7
14.3%
11 7
14.3%
10 5
10.2%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:52
Maximum2022-05-30 09:47:04
2023-12-10T23:49:01.270337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:01.402140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-05-15 02:20:30
Maximum2022-07-17 21:58:19
2023-12-10T23:49:01.515120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:01.626117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

CDBX
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1846894 × 1014
Minimum1.14322 × 1014
Maximum1.24367 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:01.752021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.14322 × 1014
5-th percentile1.146796 × 1014
Q11.16095 × 1014
median1.18278 × 1014
Q31.20275 × 1014
95-th percentile1.242902 × 1014
Maximum1.24367 × 1014
Range1.0045 × 1013
Interquartile range (IQR)4.18 × 1012

Descriptive statistics

Standard deviation2.8354891 × 1012
Coefficient of variation (CV)0.023934452
Kurtosis-0.55687991
Mean1.1846894 × 1014
Median Absolute Deviation (MAD)2.183 × 1012
Skewness0.50051212
Sum5.804978 × 1015
Variance8.0399985 × 1024
MonotonicityStrictly decreasing
2023-12-10T23:49:01.877947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
124367000000000 1
 
2.0%
115855000000000 1
 
2.0%
117690000000000 1
 
2.0%
117567000000000 1
 
2.0%
117443000000000 1
 
2.0%
117211000000000 1
 
2.0%
117011000000000 1
 
2.0%
116814000000000 1
 
2.0%
116657000000000 1
 
2.0%
116403000000000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
114322000000000 1
2.0%
114552000000000 1
2.0%
114610000000000 1
2.0%
114784000000000 1
2.0%
115105000000000 1
2.0%
115133000000000 1
2.0%
115306000000000 1
2.0%
115401000000000 1
2.0%
115469000000000 1
2.0%
115493000000000 1
2.0%
ValueCountFrequency (%)
124367000000000 1
2.0%
124335000000000 1
2.0%
124311000000000 1
2.0%
124259000000000 1
2.0%
122378000000000 1
2.0%
122120000000000 1
2.0%
121760000000000 1
2.0%
121416000000000 1
2.0%
121254000000000 1
2.0%
120876000000000 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
2023-12-10T23:49:01.994908image/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
2023-12-10T23:49:02.116358image/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

2023-12-10T23:48:57.127412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:56.607877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:56.879711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:57.218000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:56.704303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:56.980142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:57.296782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:56.787815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:57.056097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:49:02.206515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
DPTR_CN_NM1.0000.9421.0001.0001.0000.9950.7561.0001.0000.7150.657
ARVL_CN_NM0.9421.0001.0001.0000.9541.0000.6951.0001.0000.7370.638
DPRT_PRT_CD1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ARRV_PRT_CD1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
DPTR_PRT_NM1.0000.9541.0001.0001.0000.9880.0001.0001.0000.7130.869
ARVL_PRT_NM0.9951.0001.0001.0000.9881.0000.9841.0001.0001.0000.936
SHIP_CNT0.7560.6951.0001.0000.0000.9841.0001.0001.0000.3670.416
DPTR_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ARVL_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
CDBX0.7150.7371.0001.0000.7131.0000.3671.0001.0001.0000.974
RN0.6570.6381.0001.0000.8690.9360.4161.0001.0000.9741.000
2023-12-10T23:49:02.335384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTCDBXRN
SHIP_CNT1.0000.244-0.244
CDBX0.2441.000-1.000
RN-0.244-1.0001.000

Missing values

2023-12-10T23:48:57.392461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:48:57.527779image/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.
2023-12-10T23:48:57.623141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
0Puerto RicoColombia<NA>COCTGMona Passage WestCartagena, Colombia1401-Feb-2022 17:09:2730-Jun-2022 12:52:431243670000000002
1ChinaAustraliaCNTXGAUPHETianjin XingangPort Hedland618-Feb-2022 20:08:2028-Jun-2022 06:54:151243350000000003
2Papua New GuineaJapan<NA>JPOWAJomard PassageOwase615-Jan-2022 05:26:2917-Jul-2022 21:46:531243110000000004
3FranceSpain<NA>ESFROSeine BayFerrol1113-Jan-2022 23:17:1717-Jul-2022 21:08:181242590000000005
4NetherlandsDenmark<NA>DKAALTerschellinger Bank - NE BoundAalborg1322-Jan-2022 02:55:0716-Jul-2022 03:18:531223780000000006
5SpainUnited States<NA>USCHSGibraltar - W boundCharleston1201-Jan-2022 00:00:5213-Jul-2022 09:35:141221200000000007
6SpainUnited States<NA>USPTMOffshore Gibraltar No.2 (TA)Portsmouth, Virginia, U.S.A.919-Feb-2022 14:55:1004-Jul-2022 21:56:361217600000000008
7SpainFranceESLPAFRBOLLas PalmasBoulogne-sur-Mer1220-Jan-2022 02:05:2817-May-2022 13:52:441214160000000009
8GreeceTurkeyGRMLOTRCKZMilos IslandCanakkale1621-Jan-2022 06:06:0528-Jun-2022 23:56:4112125400000000010
9BrazilUruguayBRPOU<NA>Ponta UbuOffshore La Paloma No. 1 (TA)1113-Jan-2022 12:04:4708-Jul-2022 10:49:2112087600000000011
DPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
39FranceUnited StatesFRSNR<NA>St NazaireOffshore South West Pass No.1 (TA)1212-Jan-2022 20:54:2304-Jun-2022 04:50:3011549300000000041
40ColombiaEcuadorCOBUNECGYEBuenaventuraGuayaquil1417-Jan-2022 09:54:1521-Jun-2022 17:56:4211546900000000042
41MalaysiaIndonesia<NA>IDBYQOffshore Singapore No.2 TSAPulo Bunyu901-Jan-2022 01:15:2917-Jul-2022 21:00:1311540100000000043
42MoroccoSpain<NA>ESALCGibraltar - E boundAlicante Bay (TSA)916-Jan-2022 09:24:3817-Jul-2022 01:55:5511530600000000044
43ChileEcuadorCLANF<NA>AntofagastaOffshore Punta Arenas No.1 (TA)705-Jan-2022 00:18:4125-Jun-2022 10:59:0811513300000000045
44OmanIran<NA>IRBSRQuoins - W boundBandar e Shahid Rajai1118-Jan-2022 09:14:3813-Jul-2022 16:33:4011510500000000046
45IndonesiaAustralia<NA>AUMIBOffshore Banka (TA)Groote Eylandt901-Jan-2022 13:26:0408-Jul-2022 09:42:1111478400000000047
46RussiaNorwayRUKAN<NA>KandalakshaNorth Cape - West bound827-Jan-2022 18:20:3515-May-2022 02:20:3011461000000000048
47South AfricaUruguayZAPLZUYJITPort ElizabethJose Ignacio829-Jan-2022 13:19:5621-Jun-2022 06:20:5611455200000000049
48JamaicaHonduras<NA>HNTEAPort Royal, JamaicaTela817-Feb-2022 03:50:3706-Jun-2022 02:24:4611432200000000050