Overview

Dataset statistics

Number of variables12
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory102.7 B

Variable types

Numeric4
Categorical2
Text4
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001079

Alerts

RANK is highly overall correlated with FRGHT_CNVNC_QTY and 1 other fieldsHigh correlation
SHIP_CNT is highly overall correlated with DPTR_CN_NM and 1 other fieldsHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with RANK and 1 other fieldsHigh correlation
RN is highly overall correlated with RANK and 1 other fieldsHigh correlation
DPTR_CN_NM is highly overall correlated with SHIP_CNTHigh correlation
ARVL_CN_NM is highly overall correlated with SHIP_CNTHigh correlation
RANK has unique valuesUnique
DPTR_HMS has unique valuesUnique
ARVL_HMS has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:41:36.254854
Analysis finished2023-12-10 14:41:38.145247
Duration1.89 second
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%
Mean576
Minimum552
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:41:38.205337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum552
5-th percentile554.4
Q1564
median576
Q3588
95-th percentile597.6
Maximum600
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.024806754
Kurtosis-1.2
Mean576
Median Absolute Deviation (MAD)12
Skewness0
Sum28224
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:41:38.328202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
552 1
 
2.0%
589 1
 
2.0%
579 1
 
2.0%
580 1
 
2.0%
581 1
 
2.0%
582 1
 
2.0%
583 1
 
2.0%
584 1
 
2.0%
585 1
 
2.0%
586 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
552 1
2.0%
553 1
2.0%
554 1
2.0%
555 1
2.0%
556 1
2.0%
557 1
2.0%
558 1
2.0%
559 1
2.0%
560 1
2.0%
561 1
2.0%
ValueCountFrequency (%)
600 1
2.0%
599 1
2.0%
598 1
2.0%
597 1
2.0%
596 1
2.0%
595 1
2.0%
594 1
2.0%
593 1
2.0%
592 1
2.0%
591 1
2.0%

DPTR_CN_NM
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
South Africa
10 
Australia
6 
Malaysia
6 
Brazil
5 
Indonesia
3 
Other values (13)
19 

Length

Max length20
Median length13
Mean length9.3673469
Min length5

Unique

Unique8 ?
Unique (%)16.3%

Sample

1st rowBrazil
2nd rowMalaysia
3rd rowPanama
4th rowSouth Africa
5th rowSingapore

Common Values

ValueCountFrequency (%)
South Africa 10
20.4%
Australia 6
12.2%
Malaysia 6
12.2%
Brazil 5
10.2%
Indonesia 3
 
6.1%
Papua New Guinea 3
 
6.1%
Spain 2
 
4.1%
China 2
 
4.1%
Singapore 2
 
4.1%
United States 2
 
4.1%
Other values (8) 8
16.3%

Length

2023-12-10T23:41:38.452697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 10
14.1%
africa 10
14.1%
australia 6
 
8.5%
malaysia 6
 
8.5%
brazil 5
 
7.0%
guinea 4
 
5.6%
new 3
 
4.2%
united 3
 
4.2%
papua 3
 
4.2%
indonesia 3
 
4.2%
Other values (14) 18
25.4%

ARVL_CN_NM
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
China
10 
Indonesia
7 
Japan
4 
South Africa
4 
Brazil
3 
Other values (16)
21 

Length

Max length20
Median length13
Mean length7.6734694
Min length5

Unique

Unique12 ?
Unique (%)24.5%

Sample

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

Common Values

ValueCountFrequency (%)
China 10
20.4%
Indonesia 7
14.3%
Japan 4
 
8.2%
South Africa 4
 
8.2%
Brazil 3
 
6.1%
Australia 3
 
6.1%
Malaysia 2
 
4.1%
India 2
 
4.1%
United Arab Emirates 2
 
4.1%
Senegal 1
 
2.0%
Other values (11) 11
22.4%

Length

2023-12-10T23:41:38.573701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china 10
16.9%
indonesia 7
11.9%
japan 4
 
6.8%
south 4
 
6.8%
africa 4
 
6.8%
australia 3
 
5.1%
united 3
 
5.1%
brazil 3
 
5.1%
malaysia 2
 
3.4%
india 2
 
3.4%
Other values (15) 17
28.8%

DPRT_PRT_CD
Text

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:41:38.746126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters294
Distinct characters26
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

Unique28 ?
Unique (%)57.1%

Sample

1st rowBR0057
2nd rowMY0064
3rd rowPA0067
4th rowZA0006
5th rowSG0046
ValueCountFrequency (%)
cp1300 6
 
12.2%
sp0600 3
 
6.1%
br0057 3
 
6.1%
au0240 3
 
6.1%
my0064 2
 
4.1%
cp2110 2
 
4.1%
cp2500 2
 
4.1%
us0627 1
 
2.0%
gn0013 1
 
2.0%
za0023 1
 
2.0%
Other values (25) 25
51.0%
2023-12-10T23:41:39.010025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 91
31.0%
1 21
 
7.1%
3 18
 
6.1%
2 18
 
6.1%
P 17
 
5.8%
6 15
 
5.1%
C 13
 
4.4%
A 12
 
4.1%
4 11
 
3.7%
5 9
 
3.1%
Other values (16) 69
23.5%

Most occurring categories

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

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 17
17.3%
C 13
13.3%
A 12
12.2%
S 8
8.2%
U 8
8.2%
R 7
7.1%
Z 6
 
6.1%
B 5
 
5.1%
M 5
 
5.1%
Y 4
 
4.1%
Other values (6) 13
13.3%
Decimal Number
ValueCountFrequency (%)
0 91
46.4%
1 21
 
10.7%
3 18
 
9.2%
2 18
 
9.2%
6 15
 
7.7%
4 11
 
5.6%
5 9
 
4.6%
7 8
 
4.1%
9 3
 
1.5%
8 2
 
1.0%

Most occurring scripts

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

Most frequent character per script

Latin
ValueCountFrequency (%)
P 17
17.3%
C 13
13.3%
A 12
12.2%
S 8
8.2%
U 8
8.2%
R 7
7.1%
Z 6
 
6.1%
B 5
 
5.1%
M 5
 
5.1%
Y 4
 
4.1%
Other values (6) 13
13.3%
Common
ValueCountFrequency (%)
0 91
46.4%
1 21
 
10.7%
3 18
 
9.2%
2 18
 
9.2%
6 15
 
7.7%
4 11
 
5.6%
5 9
 
4.6%
7 8
 
4.1%
9 3
 
1.5%
8 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91
31.0%
1 21
 
7.1%
3 18
 
6.1%
2 18
 
6.1%
P 17
 
5.8%
6 15
 
5.1%
C 13
 
4.4%
A 12
 
4.1%
4 11
 
3.7%
5 9
 
3.1%
Other values (16) 69
23.5%

ARRV_PRT_CD
Text

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:41:39.190856image/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

Unique36 ?
Unique (%)73.5%

Sample

1st rowID0200
2nd rowZA0006
3rd rowCN0652
4th rowID0374
5th rowMU0004
ValueCountFrequency (%)
cn0107 3
 
6.1%
id0316 2
 
4.1%
cp1300 2
 
4.1%
au0223 2
 
4.1%
za0006 2
 
4.1%
ae0035 2
 
4.1%
us0587 1
 
2.0%
id0200 1
 
2.0%
cp2700 1
 
2.0%
in0197 1
 
2.0%
Other values (32) 32
65.3%
2023-12-10T23:41:39.473898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 84
28.6%
2 21
 
7.1%
1 19
 
6.5%
3 19
 
6.5%
C 15
 
5.1%
N 13
 
4.4%
5 13
 
4.4%
7 12
 
4.1%
6 10
 
3.4%
P 9
 
3.1%
Other values (21) 79
26.9%

Most occurring categories

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

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 15
15.3%
N 13
13.3%
P 9
9.2%
A 8
 
8.2%
I 8
 
8.2%
D 6
 
6.1%
U 5
 
5.1%
S 4
 
4.1%
R 4
 
4.1%
Z 4
 
4.1%
Other values (11) 22
22.4%
Decimal Number
ValueCountFrequency (%)
0 84
42.9%
2 21
 
10.7%
1 19
 
9.7%
3 19
 
9.7%
5 13
 
6.6%
7 12
 
6.1%
6 10
 
5.1%
4 9
 
4.6%
8 6
 
3.1%
9 3
 
1.5%

Most occurring scripts

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

Most frequent character per script

Latin
ValueCountFrequency (%)
C 15
15.3%
N 13
13.3%
P 9
9.2%
A 8
 
8.2%
I 8
 
8.2%
D 6
 
6.1%
U 5
 
5.1%
S 4
 
4.1%
R 4
 
4.1%
Z 4
 
4.1%
Other values (11) 22
22.4%
Common
ValueCountFrequency (%)
0 84
42.9%
2 21
 
10.7%
1 19
 
9.7%
3 19
 
9.7%
5 13
 
6.6%
7 12
 
6.1%
6 10
 
5.1%
4 9
 
4.6%
8 6
 
3.1%
9 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84
28.6%
2 21
 
7.1%
1 19
 
6.5%
3 19
 
6.5%
C 15
 
5.1%
N 13
 
4.4%
5 13
 
4.4%
7 12
 
4.1%
6 10
 
3.4%
P 9
 
3.1%
Other values (21) 79
26.9%

DPTR_PRT_NM
Text

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:41:39.726956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length34
Mean length20.857143
Min length6

Characters and Unicode

Total characters1022
Distinct characters53
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

Unique28 ?
Unique (%)57.1%

Sample

1st rowGuaiba International Terminal
2nd rowNorth West Malacca Strait No.1 (Tsa)
3rd rowOffshore Taboguilla No. 1 (TA)
4th rowCape Town
5th rowWestern Boarding Ground A - Singapore
ValueCountFrequency (%)
8
 
4.6%
cape 7
 
4.0%
of 7
 
4.0%
offshore 6
 
3.4%
good 6
 
3.4%
hope 6
 
3.4%
port 6
 
3.4%
bound 6
 
3.4%
singapore 5
 
2.9%
terminal 5
 
2.9%
Other values (74) 112
64.4%
2023-12-10T23:41:40.076106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
125
 
12.2%
a 97
 
9.5%
o 87
 
8.5%
r 63
 
6.2%
e 58
 
5.7%
n 52
 
5.1%
t 50
 
4.9%
i 43
 
4.2%
s 32
 
3.1%
d 28
 
2.7%
Other values (43) 387
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 683
66.8%
Uppercase Letter 169
 
16.5%
Space Separator 125
 
12.2%
Other Punctuation 10
 
1.0%
Open Punctuation 9
 
0.9%
Close Punctuation 9
 
0.9%
Dash Punctuation 9
 
0.9%
Decimal Number 8
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 97
14.2%
o 87
12.7%
r 63
9.2%
e 58
 
8.5%
n 52
 
7.6%
t 50
 
7.3%
i 43
 
6.3%
s 32
 
4.7%
d 28
 
4.1%
l 23
 
3.4%
Other values (14) 150
22.0%
Uppercase Letter
ValueCountFrequency (%)
S 20
11.8%
T 18
10.7%
G 15
 
8.9%
O 13
 
7.7%
P 13
 
7.7%
H 12
 
7.1%
W 11
 
6.5%
N 11
 
6.5%
C 10
 
5.9%
E 8
 
4.7%
Other values (9) 38
22.5%
Decimal Number
ValueCountFrequency (%)
1 5
62.5%
2 1
 
12.5%
3 1
 
12.5%
5 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 9
90.0%
, 1
 
10.0%
Space Separator
ValueCountFrequency (%)
125
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 852
83.4%
Common 170
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 97
 
11.4%
o 87
 
10.2%
r 63
 
7.4%
e 58
 
6.8%
n 52
 
6.1%
t 50
 
5.9%
i 43
 
5.0%
s 32
 
3.8%
d 28
 
3.3%
l 23
 
2.7%
Other values (33) 319
37.4%
Common
ValueCountFrequency (%)
125
73.5%
. 9
 
5.3%
( 9
 
5.3%
) 9
 
5.3%
- 9
 
5.3%
1 5
 
2.9%
, 1
 
0.6%
2 1
 
0.6%
3 1
 
0.6%
5 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
125
 
12.2%
a 97
 
9.5%
o 87
 
8.5%
r 63
 
6.2%
e 58
 
5.7%
n 52
 
5.1%
t 50
 
4.9%
i 43
 
4.2%
s 32
 
3.1%
d 28
 
2.7%
Other values (43) 387
37.9%

ARVL_PRT_NM
Text

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:41:40.327941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length17.265306
Min length6

Characters and Unicode

Total characters846
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

Unique36 ?
Unique (%)73.5%

Sample

1st rowPantoloan
2nd rowCape Town
3rd rowDongjiakou
4th rowKrueng Geukueh
5th rowPort Louis
ValueCountFrequency (%)
8
 
5.5%
terminal 7
 
4.8%
port 5
 
3.4%
offshore 4
 
2.7%
tsa 4
 
2.7%
cape 4
 
2.7%
qhd 3
 
2.1%
oil 3
 
2.1%
32-6 3
 
2.1%
strait 3
 
2.1%
Other values (87) 102
69.9%
2023-12-10T23:41:40.727458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97
 
11.5%
a 95
 
11.2%
o 55
 
6.5%
n 49
 
5.8%
r 44
 
5.2%
e 42
 
5.0%
i 39
 
4.6%
t 34
 
4.0%
l 27
 
3.2%
d 23
 
2.7%
Other values (49) 341
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 563
66.5%
Uppercase Letter 152
 
18.0%
Space Separator 97
 
11.5%
Dash Punctuation 13
 
1.5%
Decimal Number 11
 
1.3%
Open Punctuation 4
 
0.5%
Close Punctuation 4
 
0.5%
Other Punctuation 2
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 19
12.5%
S 17
 
11.2%
P 16
 
10.5%
O 13
 
8.6%
C 9
 
5.9%
B 8
 
5.3%
A 6
 
3.9%
H 6
 
3.9%
K 6
 
3.9%
L 6
 
3.9%
Other values (15) 46
30.3%
Lowercase Letter
ValueCountFrequency (%)
a 95
16.9%
o 55
9.8%
n 49
 
8.7%
r 44
 
7.8%
e 42
 
7.5%
i 39
 
6.9%
t 34
 
6.0%
l 27
 
4.8%
d 23
 
4.1%
u 23
 
4.1%
Other values (14) 132
23.4%
Decimal Number
ValueCountFrequency (%)
2 3
27.3%
6 3
27.3%
3 3
27.3%
1 2
18.2%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
97
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 715
84.5%
Common 131
 
15.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 95
 
13.3%
o 55
 
7.7%
n 49
 
6.9%
r 44
 
6.2%
e 42
 
5.9%
i 39
 
5.5%
t 34
 
4.8%
l 27
 
3.8%
d 23
 
3.2%
u 23
 
3.2%
Other values (39) 284
39.7%
Common
ValueCountFrequency (%)
97
74.0%
- 13
 
9.9%
( 4
 
3.1%
) 4
 
3.1%
2 3
 
2.3%
6 3
 
2.3%
3 3
 
2.3%
1 2
 
1.5%
. 1
 
0.8%
, 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
97
 
11.5%
a 95
 
11.2%
o 55
 
6.5%
n 49
 
5.8%
r 44
 
5.2%
e 42
 
5.0%
i 39
 
4.6%
t 34
 
4.0%
l 27
 
3.2%
d 23
 
2.7%
Other values (49) 341
40.3%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.857143
Minimum1
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:41:40.881099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median8
Q312
95-th percentile48.2
Maximum84
Range83
Interquartile range (IQR)9

Descriptive statistics

Standard deviation16.444351
Coefficient of variation (CV)1.3868729
Kurtosis9.6969743
Mean11.857143
Median Absolute Deviation (MAD)4
Skewness3.0401271
Sum581
Variance270.41667
MonotonicityNot monotonic
2023-12-10T23:41:41.003048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 5
 
10.2%
3 5
 
10.2%
1 4
 
8.2%
12 4
 
8.2%
10 4
 
8.2%
4 4
 
8.2%
8 3
 
6.1%
11 3
 
6.1%
9 2
 
4.1%
5 2
 
4.1%
Other values (10) 13
26.5%
ValueCountFrequency (%)
1 4
8.2%
2 5
10.2%
3 5
10.2%
4 4
8.2%
5 2
 
4.1%
6 2
 
4.1%
7 2
 
4.1%
8 3
6.1%
9 2
 
4.1%
10 4
8.2%
ValueCountFrequency (%)
84 1
 
2.0%
67 1
 
2.0%
51 1
 
2.0%
44 1
 
2.0%
23 2
4.1%
17 1
 
2.0%
15 1
 
2.0%
13 1
 
2.0%
12 4
8.2%
11 3
6.1%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:04:11
Maximum2022-06-08 12:17:22
2023-12-10T23:41:41.112531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:41.242621image/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
Minimum2022-03-03 04:22:15
Maximum2022-07-17 22:00:05
2023-12-10T23:41:41.647240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:41.780988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0683598 × 109
Minimum2.95109 × 109
Maximum3.19683 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:41:41.910646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.95109 × 109
5-th percentile2.966152 × 109
Q13.0094 × 109
median3.0563 × 109
Q33.14459 × 109
95-th percentile3.186184 × 109
Maximum3.19683 × 109
Range2.4574 × 108
Interquartile range (IQR)1.3519 × 108

Descriptive statistics

Standard deviation74151903
Coefficient of variation (CV)0.024166626
Kurtosis-1.2403928
Mean3.0683598 × 109
Median Absolute Deviation (MAD)54300000
Skewness0.32609778
Sum1.5034963 × 1011
Variance5.4985048 × 1015
MonotonicityDecreasing
2023-12-10T23:41:42.046353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3064670000 2
 
4.1%
3196830000 1
 
2.0%
3008710000 1
 
2.0%
3032450000 1
 
2.0%
3031320000 1
 
2.0%
3027630000 1
 
2.0%
3027320000 1
 
2.0%
3020770000 1
 
2.0%
3014540000 1
 
2.0%
3013670000 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
2951090000 1
2.0%
2961900000 1
2.0%
2962940000 1
2.0%
2970970000 1
2.0%
2983370000 1
2.0%
2984120000 1
2.0%
2986050000 1
2.0%
2986840000 1
2.0%
2999180000 1
2.0%
3005930000 1
2.0%
ValueCountFrequency (%)
3196830000 1
2.0%
3193570000 1
2.0%
3187040000 1
2.0%
3184900000 1
2.0%
3180950000 1
2.0%
3171320000 1
2.0%
3168160000 1
2.0%
3167130000 1
2.0%
3158320000 1
2.0%
3152180000 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
2023-12-10T23:41:42.182425image/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
2023-12-10T23:41:42.303424image/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

2023-12-10T23:41:37.627725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:36.736227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.018032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.337010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.694773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:36.804879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.100443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.415462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.760909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:36.868618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.168972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.484370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.834823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:36.940776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.260485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:41:37.556943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:41:42.393351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKDPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
RANK1.0000.0000.4350.2750.4740.2750.4740.0001.0001.0000.9841.000
DPTR_CN_NM0.0001.0000.8111.0000.8101.0000.8100.9021.0001.0000.0000.000
ARVL_CN_NM0.4350.8111.0000.8501.0000.8501.0000.9841.0001.0000.0000.406
DPRT_PRT_CD0.2751.0000.8501.0000.0001.0000.0000.9681.0001.0000.7930.000
ARRV_PRT_CD0.4740.8101.0000.0001.0000.0001.0000.9991.0001.0000.4400.540
DPTR_PRT_NM0.2751.0000.8501.0000.0001.0000.0000.9681.0001.0000.7930.000
ARVL_PRT_NM0.4740.8101.0000.0001.0000.0001.0000.9991.0001.0000.4400.540
SHIP_CNT0.0000.9020.9840.9680.9990.9680.9991.0001.0001.0000.4310.207
DPTR_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ARVL_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
FRGHT_CNVNC_QTY0.9840.0000.0000.7930.4400.7930.4400.4311.0001.0001.0000.981
RN1.0000.0000.4060.0000.5400.0000.5400.2071.0001.0000.9811.000
2023-12-10T23:41:42.518061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTR_CN_NMARVL_CN_NM
DPTR_CN_NM1.0000.351
ARVL_CN_NM0.3511.000
2023-12-10T23:41:42.596734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRNDPTR_CN_NMARVL_CN_NM
RANK1.0000.120-1.0001.0000.0000.136
SHIP_CNT0.1201.000-0.1220.1200.5950.664
FRGHT_CNVNC_QTY-1.000-0.1221.000-1.0000.0000.000
RN1.0000.120-1.0001.0000.0000.136
DPTR_CN_NM0.0000.5950.0000.0001.0000.351
ARVL_CN_NM0.1360.6640.0000.1360.3511.000

Missing values

2023-12-10T23:41:37.958853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:41:38.091266image/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

RANKDPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
0552BrazilIndonesiaBR0057ID0200Guaiba International TerminalPantoloan108-Jun-2022 12:17:2217-Jul-2022 11:04:3931968300002
1553MalaysiaSouth AfricaMY0064ZA0006North West Malacca Strait No.1 (Tsa)Cape Town327-Jan-2022 08:18:4505-Apr-2022 02:51:1231935700003
2554PanamaChinaPA0067CN0652Offshore Taboguilla No. 1 (TA)Dongjiakou518-Jan-2022 01:55:1719-Jun-2022 23:58:1631870400004
3555South AfricaIndonesiaZA0006ID0374Cape TownKrueng Geukueh123-Jan-2022 10:10:1919-Mar-2022 13:22:1031849000005
4556SingaporeMauritiusSG0046MU0004Western Boarding Ground A - SingaporePort Louis215-Feb-2022 09:19:2305-Jun-2022 23:39:2431809500006
5557BrazilMalaysiaBR0133MY0078SantosPort Klang - Kapar Power Station604-Mar-2022 22:25:0521-May-2022 10:06:1131713200007
6558South AfricaMalaysiaCP1300MY0064Cape Of Good HopeNorth West Malacca Strait No.1 (Tsa)1501-Jan-2022 00:04:1105-Jul-2022 19:46:3531681600008
7559MalaysiaBrazilMY0064BR0135North West Malacca Strait No.1 (Tsa)Sao Luiz De Maranhao321-Jan-2022 04:08:2817-Jun-2022 20:37:4531671300009
8560Sri LankaIndiaRP4600IN0119Dondra Head - W boundOkha, India2324-Jan-2022 15:10:5217-Jul-2022 15:53:39315832000010
9561United StatesSpainUM0039ES0028Offshore South West Pass No.1 (TA)Cadiz Bay1201-Jan-2022 00:07:3307-Jul-2022 23:24:49315218000011
RANKDPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
39591Papua New GuineaJapanSP0600JP0047Jomard PassageHakodate1106-Jan-2022 21:03:0130-May-2022 16:19:56300593000041
40592SingaporeChinaSG0045CN0107Eastern Boarding Ground B - SingaporeQhd 32-6825-Jan-2022 02:09:4714-Jul-2022 09:58:57299918000042
41593ChinaAustraliaCN0640AU0223Lanshan East Bulk TerminalPort Hedland920-Mar-2022 13:50:3318-Jun-2022 08:49:30298684000043
42594BrazilIndonesiaBR0057ID0316Guaiba International TerminalTeluk Semangka211-Feb-2022 12:51:1905-Jul-2022 15:49:02298605000044
43595IndonesiaChinaCP2500CN0123Lombok StraitShidao622-Jan-2022 11:21:2017-Jul-2022 21:39:04298412000045
44596South AfricaIndonesiaZA0023ID0316Saldanha BayTeluk Semangka324-Jan-2022 15:17:2802-Jun-2022 13:12:59298337000046
45597Papua New GuineaJapanSP0600JP0155Jomard PassageMatsuyama1701-Jan-2022 00:32:4907-Jun-2022 15:57:48297097000047
46598AustraliaIndonesiaAU0374CP2500Dampier - Parker PointLombok Strait2322-Jan-2022 16:30:2917-Jul-2022 21:28:11296294000048
47599AustraliaTaiwanAU0240TW0028Port WalcottTa-Lin-Pu Offshore Oil Terminal1204-Jan-2022 04:02:1905-Jun-2022 05:21:12296190000049
48600MalaysiaJapanMY0152JP0353Offshore Singapore No. 5Yokosuka816-Jan-2022 02:01:1703-Jun-2022 02:03:32295109000050