Overview

Dataset statistics

Number of variables11
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory93.7 B

Variable types

Categorical2
Text4
Numeric3
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001070

Alerts

SHIP_CNT is highly overall correlated with DPTR_CN_NMHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with RNHigh correlation
RN is highly overall correlated with FRGHT_CNVNC_QTYHigh correlation
DPTR_CN_NM is highly overall correlated with SHIP_CNTHigh correlation
DPTR_HMS has unique valuesUnique
ARVL_HMS has unique valuesUnique
FRGHT_CNVNC_QTY has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:47:50.950069
Analysis finished2023-12-10 14:47:52.524560
Duration1.57 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DPTR_CN_NM
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
South Africa
11 
Papua New Guinea
6 
Brazil
5 
Australia
5 
Malaysia
3 
Other values (14)
19 

Length

Max length16
Median length12
Mean length9.7755102
Min length5

Unique

Unique9 ?
Unique (%)18.4%

Sample

1st rowSouth Africa
2nd rowYemen
3rd rowJapan
4th rowSouth Africa
5th rowGuinea

Common Values

ValueCountFrequency (%)
South Africa 11
22.4%
Papua New Guinea 6
12.2%
Brazil 5
10.2%
Australia 5
10.2%
Malaysia 3
 
6.1%
China 2
 
4.1%
Singapore 2
 
4.1%
Indonesia 2
 
4.1%
The Bahamas 2
 
4.1%
United States 2
 
4.1%
Other values (9) 9
18.4%

Length

2023-12-10T23:47:52.596344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 12
15.6%
africa 11
14.3%
guinea 7
 
9.1%
papua 6
 
7.8%
new 6
 
7.8%
brazil 5
 
6.5%
australia 5
 
6.5%
malaysia 3
 
3.9%
states 2
 
2.6%
united 2
 
2.6%
Other values (13) 18
23.4%

ARVL_CN_NM
Categorical

Distinct18
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
Japan
9 
Indonesia
8 
China
8 
South Africa
4 
Australia
3 
Other values (13)
17 

Length

Max length20
Median length13
Mean length7.6122449
Min length5

Unique

Unique10 ?
Unique (%)20.4%

Sample

1st rowChina
2nd rowEgypt
3rd rowAustralia
4th rowIndonesia
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
Japan 9
18.4%
Indonesia 8
16.3%
China 8
16.3%
South Africa 4
8.2%
Australia 3
 
6.1%
Spain 3
 
6.1%
Malaysia 2
 
4.1%
United Arab Emirates 2
 
4.1%
Egypt 1
 
2.0%
Sri Lanka 1
 
2.0%
Other values (8) 8
16.3%

Length

2023-12-10T23:47:52.726822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
japan 9
15.3%
china 8
13.6%
indonesia 8
13.6%
south 4
 
6.8%
africa 4
 
6.8%
australia 3
 
5.1%
spain 3
 
5.1%
united 3
 
5.1%
malaysia 2
 
3.4%
arab 2
 
3.4%
Other values (12) 13
22.0%

DPRT_PRT_CD
Text

Distinct33
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:47:52.899660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters294
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)55.1%

Sample

1st rowCP1300
2nd rowZZ0010
3rd rowRP2300
4th rowCP1300
5th rowGN0013
ValueCountFrequency (%)
sp0600 6
 
12.2%
cp1300 5
 
10.2%
za0006 3
 
6.1%
br0057 3
 
6.1%
au0240 3
 
6.1%
cp2500 2
 
4.1%
pa0067 1
 
2.0%
rp4400 1
 
2.0%
cp3810 1
 
2.0%
rp3100 1
 
2.0%
Other values (23) 23
46.9%
2023-12-10T23:47:53.234096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 102
34.7%
P 19
 
6.5%
1 19
 
6.5%
2 17
 
5.8%
3 16
 
5.4%
6 14
 
4.8%
A 13
 
4.4%
S 12
 
4.1%
C 11
 
3.7%
4 10
 
3.4%
Other values (13) 61
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 196
66.7%
Uppercase Letter 98
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 19
19.4%
A 13
13.3%
S 12
12.2%
C 11
11.2%
R 10
10.2%
Z 8
8.2%
U 7
 
7.1%
B 6
 
6.1%
G 4
 
4.1%
N 4
 
4.1%
Other values (4) 4
 
4.1%
Decimal Number
ValueCountFrequency (%)
0 102
52.0%
1 19
 
9.7%
2 17
 
8.7%
3 16
 
8.2%
6 14
 
7.1%
4 10
 
5.1%
5 9
 
4.6%
7 7
 
3.6%
8 2
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 196
66.7%
Latin 98
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 19
19.4%
A 13
13.3%
S 12
12.2%
C 11
11.2%
R 10
10.2%
Z 8
8.2%
U 7
 
7.1%
B 6
 
6.1%
G 4
 
4.1%
N 4
 
4.1%
Other values (4) 4
 
4.1%
Common
ValueCountFrequency (%)
0 102
52.0%
1 19
 
9.7%
2 17
 
8.7%
3 16
 
8.2%
6 14
 
7.1%
4 10
 
5.1%
5 9
 
4.6%
7 7
 
3.6%
8 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102
34.7%
P 19
 
6.5%
1 19
 
6.5%
2 17
 
5.8%
3 16
 
5.4%
6 14
 
4.8%
A 13
 
4.4%
S 12
 
4.1%
C 11
 
3.7%
4 10
 
3.4%
Other values (13) 61
20.7%

ARRV_PRT_CD
Text

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:47:53.450616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters294
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)79.6%

Sample

1st rowCN0216
2nd rowEG0071
3rd rowAU0240
4th rowID0125
5th rowCP1300
ValueCountFrequency (%)
au0240 2
 
4.1%
es0028 2
 
4.1%
rp3400 2
 
4.1%
id0316 2
 
4.1%
ae0035 2
 
4.1%
id0238 1
 
2.0%
my0118 1
 
2.0%
cn0216 1
 
2.0%
id0443 1
 
2.0%
jp0353 1
 
2.0%
Other values (34) 34
69.4%
2023-12-10T23:47:53.836751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 82
27.9%
2 24
 
8.2%
3 22
 
7.5%
1 20
 
6.8%
P 13
 
4.4%
5 12
 
4.1%
C 12
 
4.1%
4 11
 
3.7%
A 9
 
3.1%
N 9
 
3.1%
Other values (21) 80
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 196
66.7%
Uppercase Letter 98
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 13
13.3%
C 12
12.2%
A 9
9.2%
N 9
9.2%
I 8
8.2%
D 6
 
6.1%
E 6
 
6.1%
J 6
 
6.1%
R 5
 
5.1%
U 5
 
5.1%
Other values (11) 19
19.4%
Decimal Number
ValueCountFrequency (%)
0 82
41.8%
2 24
 
12.2%
3 22
 
11.2%
1 20
 
10.2%
5 12
 
6.1%
4 11
 
5.6%
6 9
 
4.6%
8 8
 
4.1%
7 7
 
3.6%
9 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 196
66.7%
Latin 98
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 13
13.3%
C 12
12.2%
A 9
9.2%
N 9
9.2%
I 8
8.2%
D 6
 
6.1%
E 6
 
6.1%
J 6
 
6.1%
R 5
 
5.1%
U 5
 
5.1%
Other values (11) 19
19.4%
Common
ValueCountFrequency (%)
0 82
41.8%
2 24
 
12.2%
3 22
 
11.2%
1 20
 
10.2%
5 12
 
6.1%
4 11
 
5.6%
6 9
 
4.6%
8 8
 
4.1%
7 7
 
3.6%
9 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 82
27.9%
2 24
 
8.2%
3 22
 
7.5%
1 20
 
6.8%
P 13
 
4.4%
5 12
 
4.1%
C 12
 
4.1%
4 11
 
3.7%
A 9
 
3.1%
N 9
 
3.1%
Other values (21) 80
27.2%

DPTR_PRT_NM
Text

Distinct33
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:47:54.158597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length33
Mean length19.714286
Min length6

Characters and Unicode

Total characters966
Distinct characters55
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

Unique27 ?
Unique (%)55.1%

Sample

1st rowCape Of Good Hope
2nd rowGulf of Aden Transit Corridor - West Bound Exit
3rd rowMisaki - E. Entrance To JIS TSS S bound
4th rowCape Of Good Hope
5th rowOffshore Conakry No. 3 (TA)
ValueCountFrequency (%)
cape 8
 
4.9%
8
 
4.9%
of 7
 
4.3%
jomard 6
 
3.7%
passage 6
 
3.7%
offshore 6
 
3.7%
terminal 5
 
3.1%
singapore 5
 
3.1%
port 5
 
3.1%
hope 5
 
3.1%
Other values (75) 101
62.3%
2023-12-10T23:47:54.640662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
113
 
11.7%
a 97
 
10.0%
o 80
 
8.3%
e 65
 
6.7%
r 58
 
6.0%
n 52
 
5.4%
t 39
 
4.0%
i 38
 
3.9%
s 37
 
3.8%
d 23
 
2.4%
Other values (45) 364
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 660
68.3%
Uppercase Letter 159
 
16.5%
Space Separator 113
 
11.7%
Other Punctuation 11
 
1.1%
Dash Punctuation 8
 
0.8%
Open Punctuation 5
 
0.5%
Close Punctuation 5
 
0.5%
Decimal Number 5
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 97
14.7%
o 80
12.1%
e 65
9.8%
r 58
 
8.8%
n 52
 
7.9%
t 39
 
5.9%
i 38
 
5.8%
s 37
 
5.6%
d 23
 
3.5%
p 21
 
3.2%
Other values (15) 150
22.7%
Uppercase Letter
ValueCountFrequency (%)
T 18
11.3%
P 15
 
9.4%
S 15
 
9.4%
G 13
 
8.2%
C 13
 
8.2%
O 12
 
7.5%
N 9
 
5.7%
B 9
 
5.7%
A 8
 
5.0%
J 8
 
5.0%
Other values (11) 39
24.5%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
3 1
 
20.0%
5 1
 
20.0%
Other Punctuation
ValueCountFrequency (%)
. 9
81.8%
, 2
 
18.2%
Space Separator
ValueCountFrequency (%)
113
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 819
84.8%
Common 147
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 97
 
11.8%
o 80
 
9.8%
e 65
 
7.9%
r 58
 
7.1%
n 52
 
6.3%
t 39
 
4.8%
i 38
 
4.6%
s 37
 
4.5%
d 23
 
2.8%
p 21
 
2.6%
Other values (36) 309
37.7%
Common
ValueCountFrequency (%)
113
76.9%
. 9
 
6.1%
- 8
 
5.4%
( 5
 
3.4%
) 5
 
3.4%
1 3
 
2.0%
, 2
 
1.4%
3 1
 
0.7%
5 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113
 
11.7%
a 97
 
10.0%
o 80
 
8.3%
e 65
 
6.7%
r 58
 
6.0%
n 52
 
5.4%
t 39
 
4.0%
i 38
 
3.9%
s 37
 
3.8%
d 23
 
2.4%
Other values (45) 364
37.7%

ARVL_PRT_NM
Text

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:47:55.214842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length32
Mean length16.489796
Min length6

Characters and Unicode

Total characters808
Distinct characters59
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

Unique39 ?
Unique (%)79.6%

Sample

1st rowBohai Peng Lai (FPSO)
2nd rowSafaga
3rd rowPort Walcott
4th rowKuala Beukah
5th rowCape Of Good Hope
ValueCountFrequency (%)
port 8
 
5.8%
8
 
5.8%
terminal 7
 
5.1%
bay 4
 
2.9%
strait 4
 
2.9%
tsa 3
 
2.2%
oil 3
 
2.2%
north 3
 
2.2%
offshore 3
 
2.2%
semangka 2
 
1.4%
Other values (82) 93
67.4%
2023-12-10T23:47:55.640879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 105
 
13.0%
90
 
11.1%
n 47
 
5.8%
o 44
 
5.4%
t 42
 
5.2%
i 39
 
4.8%
r 37
 
4.6%
e 32
 
4.0%
l 27
 
3.3%
u 25
 
3.1%
Other values (49) 320
39.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 547
67.7%
Uppercase Letter 145
 
17.9%
Space Separator 90
 
11.1%
Dash Punctuation 11
 
1.4%
Open Punctuation 4
 
0.5%
Close Punctuation 4
 
0.5%
Decimal Number 4
 
0.5%
Other Punctuation 3
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 22
15.2%
T 19
13.1%
P 16
 
11.0%
B 9
 
6.2%
O 8
 
5.5%
A 7
 
4.8%
C 6
 
4.1%
M 6
 
4.1%
L 6
 
4.1%
E 5
 
3.4%
Other values (15) 41
28.3%
Lowercase Letter
ValueCountFrequency (%)
a 105
19.2%
n 47
 
8.6%
o 44
 
8.0%
t 42
 
7.7%
i 39
 
7.1%
r 37
 
6.8%
e 32
 
5.9%
l 27
 
4.9%
u 25
 
4.6%
h 20
 
3.7%
Other values (14) 129
23.6%
Decimal Number
ValueCountFrequency (%)
2 1
25.0%
1 1
25.0%
6 1
25.0%
3 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
; 1
33.3%
Space Separator
ValueCountFrequency (%)
90
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 692
85.6%
Common 116
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 105
 
15.2%
n 47
 
6.8%
o 44
 
6.4%
t 42
 
6.1%
i 39
 
5.6%
r 37
 
5.3%
e 32
 
4.6%
l 27
 
3.9%
u 25
 
3.6%
S 22
 
3.2%
Other values (39) 272
39.3%
Common
ValueCountFrequency (%)
90
77.6%
- 11
 
9.5%
( 4
 
3.4%
) 4
 
3.4%
. 2
 
1.7%
2 1
 
0.9%
1 1
 
0.9%
6 1
 
0.9%
3 1
 
0.9%
; 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 105
 
13.0%
90
 
11.1%
n 47
 
5.8%
o 44
 
5.4%
t 42
 
5.2%
i 39
 
4.8%
r 37
 
4.6%
e 32
 
4.0%
l 27
 
3.3%
u 25
 
3.1%
Other values (49) 320
39.6%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.244898
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:55.770989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median8
Q312
95-th percentile23
Maximum67
Range66
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.174582
Coefficient of variation (CV)0.99313644
Kurtosis20.050949
Mean10.244898
Median Absolute Deviation (MAD)4
Skewness3.8528587
Sum502
Variance103.52211
MonotonicityNot monotonic
2023-12-10T23:47:55.895550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 6
12.2%
2 4
 
8.2%
8 4
 
8.2%
5 4
 
8.2%
11 4
 
8.2%
7 3
 
6.1%
10 3
 
6.1%
12 3
 
6.1%
9 2
 
4.1%
3 2
 
4.1%
Other values (10) 14
28.6%
ValueCountFrequency (%)
1 1
 
2.0%
2 4
8.2%
3 2
 
4.1%
4 6
12.2%
5 4
8.2%
6 2
 
4.1%
7 3
6.1%
8 4
8.2%
9 2
 
4.1%
10 3
6.1%
ValueCountFrequency (%)
67 1
 
2.0%
26 1
 
2.0%
23 2
4.1%
21 1
 
2.0%
17 1
 
2.0%
16 2
4.1%
15 1
 
2.0%
13 2
4.1%
12 3
6.1%
11 4
8.2%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:21:10
Maximum2022-06-08 02:24:15
2023-12-10T23:47:56.046598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:56.202208image/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
Minimum2022-03-16 11:26:38
Maximum2022-07-17 22:00:05
2023-12-10T23:47:56.369334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:56.521047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9783524 × 109
Minimum2.9135 × 109
Maximum3.06467 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:56.662161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9135 × 109
5-th percentile2.916178 × 109
Q12.93091 × 109
median2.98337 × 109
Q33.02077 × 109
95-th percentile3.058472 × 109
Maximum3.06467 × 109
Range1.5117 × 108
Interquartile range (IQR)89860000

Descriptive statistics

Standard deviation50425986
Coefficient of variation (CV)0.016930832
Kurtosis-1.4440819
Mean2.9783524 × 109
Median Absolute Deviation (MAD)49080000
Skewness0.19893289
Sum1.4593927 × 1011
Variance2.5427801 × 1015
MonotonicityStrictly decreasing
2023-12-10T23:47:56.807621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3064670000 1
 
2.0%
2928090000 1
 
2.0%
2961900000 1
 
2.0%
2951090000 1
 
2.0%
2944550000 1
 
2.0%
2940020000 1
 
2.0%
2937560000 1
 
2.0%
2935930000 1
 
2.0%
2933610000 1
 
2.0%
2931910000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2913500000 1
2.0%
2913610000 1
2.0%
2916010000 1
2.0%
2916430000 1
2.0%
2917770000 1
2.0%
2919240000 1
2.0%
2920690000 1
2.0%
2920910000 1
2.0%
2923300000 1
2.0%
2923610000 1
2.0%
ValueCountFrequency (%)
3064670000 1
2.0%
3062590000 1
2.0%
3059380000 1
2.0%
3057110000 1
2.0%
3056300000 1
2.0%
3043820000 1
2.0%
3037540000 1
2.0%
3037190000 1
2.0%
3032450000 1
2.0%
3031320000 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
2023-12-10T23:47:56.945135image/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
2023-12-10T23:47:57.082628image/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

2023-12-10T23:47:52.020750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.448135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.704103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:52.118763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.536420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.803547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:52.226818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.630760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:51.907813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:47:57.179301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
DPTR_CN_NM1.0000.6621.0000.9681.0000.9680.8741.0001.0000.0000.194
ARVL_CN_NM0.6621.0000.9321.0000.9321.0000.6891.0001.0000.3630.000
DPRT_PRT_CD1.0000.9321.0000.0001.0000.0000.9271.0001.0000.5320.165
ARRV_PRT_CD0.9681.0000.0001.0000.0001.0000.8661.0001.0000.9620.619
DPTR_PRT_NM1.0000.9321.0000.0001.0000.0000.9271.0001.0000.5320.165
ARVL_PRT_NM0.9681.0000.0001.0000.0001.0000.8661.0001.0000.9620.619
SHIP_CNT0.8740.6890.9270.8660.9270.8661.0001.0001.0000.0000.000
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
FRGHT_CNVNC_QTY0.0000.3630.5320.9620.5320.9620.0001.0001.0001.0000.969
RN0.1940.0000.1650.6190.1650.6190.0001.0001.0000.9691.000
2023-12-10T23:47:57.310931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTR_CN_NMARVL_CN_NM
DPTR_CN_NM1.0000.233
ARVL_CN_NM0.2331.000
2023-12-10T23:47:57.398556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTFRGHT_CNVNC_QTYRNDPTR_CN_NMARVL_CN_NM
SHIP_CNT1.000-0.3020.3020.5480.360
FRGHT_CNVNC_QTY-0.3021.000-1.0000.0000.110
RN0.302-1.0001.0000.0000.000
DPTR_CN_NM0.5480.0000.0001.0000.233
ARVL_CN_NM0.3600.1100.0000.2331.000

Missing values

2023-12-10T23:47:52.333461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:47:52.468002image/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

DPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
0South AfricaChinaCP1300CN0216Cape Of Good HopeBohai Peng Lai (FPSO)227-Jan-2022 04:00:0022-Jun-2022 10:54:1030646700002
1YemenEgyptZZ0010EG0071Gulf of Aden Transit Corridor - West Bound ExitSafaga6705-Jan-2022 07:09:5117-Jul-2022 07:21:5130625900003
2JapanAustraliaRP2300AU0240Misaki - E. Entrance To JIS TSS S boundPort Walcott1105-Jan-2022 11:21:5910-Jun-2022 08:26:1430593800004
3South AfricaIndonesiaCP1300ID0125Cape Of Good HopeKuala Beukah126-Feb-2022 14:55:0622-Apr-2022 15:35:3530571100005
4GuineaSouth AfricaGN0013CP1300Offshore Conakry No. 3 (TA)Cape Of Good Hope915-Jan-2022 11:01:2419-May-2022 00:50:2230563000006
5South AfricaSri LankaCP1300LK0028Cape Of Good HopeHambantota303-May-2022 17:47:5905-Jul-2022 01:17:5230438200007
6BrazilQatarBR0265QA0002Acu SuperportAl Rayyan208-Jun-2022 02:24:1517-Jul-2022 22:00:0530375400008
7South AfricaUnited Arab EmiratesZA0017AE0035Port ElizabethOffshore Khor Fakkan (TSA)412-Mar-2022 10:00:5521-Jun-2022 12:43:4930371900009
8South AfricaIndiaZA0022IN0197Richards BayVisakhapatnam - Oil Terminal SBM709-Feb-2022 18:19:1217-Jul-2022 14:22:09303245000010
9Papua New GuineaIndonesiaSP0600ID0331Jomard PassageUdang Terminal1011-Jan-2022 08:57:4528-Jun-2022 09:50:41303132000011
DPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
39South AfricaIndonesiaZA0006CP3300Cape TownSunda Strait604-Mar-2022 14:12:3725-Jun-2022 03:57:42292361000041
40PanamaJapanPA0067RP2310Offshore Taboguilla No. 1 (TA)Misaki - E. Entrance To JIS TSS N bound1105-Jan-2022 06:05:2803-Jul-2022 13:54:02292330000042
41South KoreaAustraliaKR0080AU0240Lightering Area offshore YosuPort Walcott804-Jan-2022 17:41:2021-Jun-2022 15:41:07292091000043
42Papua New GuineaJapanSP0600JP0138Jomard PassageKudamatsu1601-Jan-2022 00:21:1004-Jun-2022 18:57:37292069000044
43TaiwanChinaRP3100CN0652Taiwan - West OfDongjiakou2606-Jan-2022 10:42:4317-Jul-2022 21:52:57291924000045
44South AfricaMalaysiaZA0006MY0064Cape TownNorth West Malacca Strait No.1 (Tsa)503-Mar-2022 17:07:4010-May-2022 09:24:15291777000046
45MalaysiaSouth AfricaCP3810ZA0023Singapore West(The Brothers) - NW BoundSaldanha Bay503-Jan-2022 17:35:5216-Mar-2022 11:26:38291643000047
46BrazilMauritiusBR0057MU0004Guaiba International TerminalPort Louis415-Jan-2022 02:56:4217-Jul-2022 21:55:12291601000048
47BrazilReunionBR0133RE0006SantosPort Est; Reunion Port705-Apr-2022 19:50:4017-Jul-2022 21:59:09291361000049
48United StatesJapanUS0034RP3400Astoria, Oregon, U.S.A.Tugaru - North And South Japan2101-Jan-2022 00:24:1222-May-2022 15:31:53291350000050