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
Text6
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001313

Alerts

RANK is highly overall correlated with FRGHT_CNVNC_QTY 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
RANK has unique valuesUnique
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 14:59:50.737636
Analysis finished2023-12-10 14:59:57.201976
Duration6.46 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%
Mean493
Minimum469
Maximum517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:59:57.362440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum469
5-th percentile471.4
Q1481
median493
Q3505
95-th percentile514.6
Maximum517
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.028983144
Kurtosis-1.2
Mean493
Median Absolute Deviation (MAD)12
Skewness0
Sum24157
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:59:57.659538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
469 1
 
2.0%
506 1
 
2.0%
496 1
 
2.0%
497 1
 
2.0%
498 1
 
2.0%
499 1
 
2.0%
500 1
 
2.0%
501 1
 
2.0%
502 1
 
2.0%
503 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
469 1
2.0%
470 1
2.0%
471 1
2.0%
472 1
2.0%
473 1
2.0%
474 1
2.0%
475 1
2.0%
476 1
2.0%
477 1
2.0%
478 1
2.0%
ValueCountFrequency (%)
517 1
2.0%
516 1
2.0%
515 1
2.0%
514 1
2.0%
513 1
2.0%
512 1
2.0%
511 1
2.0%
510 1
2.0%
509 1
2.0%
508 1
2.0%

DPTR_CN_NM
Text

Distinct27
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:59:58.000897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length12
Mean length8.877551
Min length5

Characters and Unicode

Total characters435
Distinct characters37
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

Unique16 ?
Unique (%)32.7%

Sample

1st rowUnited Kingdom
2nd rowSouth Korea
3rd rowNorthern Marinana Islands-Guam
4th rowSouth Africa
5th rowSingapore
ValueCountFrequency (%)
south 7
 
10.6%
united 6
 
9.1%
states 5
 
7.6%
korea 4
 
6.1%
canada 4
 
6.1%
singapore 4
 
6.1%
africa 3
 
4.5%
egypt 3
 
4.5%
malaysia 2
 
3.0%
senegal 2
 
3.0%
Other values (23) 26
39.4%
2023-12-10T23:59:58.652257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 70
16.1%
i 32
 
7.4%
e 32
 
7.4%
t 30
 
6.9%
n 30
 
6.9%
r 21
 
4.8%
o 20
 
4.6%
S 20
 
4.6%
17
 
3.9%
d 15
 
3.4%
Other values (27) 148
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 350
80.5%
Uppercase Letter 67
 
15.4%
Space Separator 17
 
3.9%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 70
20.0%
i 32
9.1%
e 32
9.1%
t 30
8.6%
n 30
8.6%
r 21
 
6.0%
o 20
 
5.7%
d 15
 
4.3%
s 14
 
4.0%
h 14
 
4.0%
Other values (11) 72
20.6%
Uppercase Letter
ValueCountFrequency (%)
S 20
29.9%
C 8
 
11.9%
U 6
 
9.0%
A 6
 
9.0%
K 5
 
7.5%
M 4
 
6.0%
E 3
 
4.5%
B 3
 
4.5%
N 3
 
4.5%
I 3
 
4.5%
Other values (4) 6
 
9.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 417
95.9%
Common 18
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 70
16.8%
i 32
 
7.7%
e 32
 
7.7%
t 30
 
7.2%
n 30
 
7.2%
r 21
 
5.0%
o 20
 
4.8%
S 20
 
4.8%
d 15
 
3.6%
s 14
 
3.4%
Other values (25) 133
31.9%
Common
ValueCountFrequency (%)
17
94.4%
- 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 70
16.1%
i 32
 
7.4%
e 32
 
7.4%
t 30
 
6.9%
n 30
 
6.9%
r 21
 
4.8%
o 20
 
4.6%
S 20
 
4.6%
17
 
3.9%
d 15
 
3.4%
Other values (27) 148
34.0%

ARVL_CN_NM
Text

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:59:59.063655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length8.5510204
Min length4

Characters and Unicode

Total characters419
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

Unique17 ?
Unique (%)34.7%

Sample

1st rowSingapore
2nd rowJapan
3rd rowJapan
4th rowSingapore
5th rowMexico
ValueCountFrequency (%)
united 10
15.2%
states 6
 
9.1%
egypt 5
 
7.6%
japan 5
 
7.6%
mexico 4
 
6.1%
china 4
 
6.1%
singapore 3
 
4.5%
arab 3
 
4.5%
emirates 3
 
4.5%
korea 2
 
3.0%
Other values (20) 21
31.8%
2023-12-11T00:00:00.013606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 49
 
11.7%
i 36
 
8.6%
e 35
 
8.4%
t 33
 
7.9%
n 33
 
7.9%
o 23
 
5.5%
17
 
4.1%
r 16
 
3.8%
p 15
 
3.6%
d 14
 
3.3%
Other values (28) 148
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 336
80.2%
Uppercase Letter 66
 
15.8%
Space Separator 17
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 49
14.6%
i 36
10.7%
e 35
10.4%
t 33
9.8%
n 33
9.8%
o 23
 
6.8%
r 16
 
4.8%
p 15
 
4.5%
d 14
 
4.2%
g 13
 
3.9%
Other values (12) 69
20.5%
Uppercase Letter
ValueCountFrequency (%)
S 14
21.2%
U 11
16.7%
E 9
13.6%
M 7
10.6%
C 5
 
7.6%
J 5
 
7.6%
A 3
 
4.5%
K 3
 
4.5%
G 2
 
3.0%
T 2
 
3.0%
Other values (5) 5
 
7.6%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 402
95.9%
Common 17
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 49
 
12.2%
i 36
 
9.0%
e 35
 
8.7%
t 33
 
8.2%
n 33
 
8.2%
o 23
 
5.7%
r 16
 
4.0%
p 15
 
3.7%
d 14
 
3.5%
S 14
 
3.5%
Other values (27) 134
33.3%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 49
 
11.7%
i 36
 
8.6%
e 35
 
8.4%
t 33
 
7.9%
n 33
 
7.9%
o 23
 
5.5%
17
 
4.1%
r 16
 
3.8%
p 15
 
3.6%
d 14
 
3.3%
Other values (28) 148
35.3%

DPRT_PRT_CD
Text

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-11T00:00:00.808230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters294
Distinct characters30
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

Unique34 ?
Unique (%)69.4%

Sample

1st rowGB0302
2nd rowKR0002
3rd rowUP0001
4th rowZA0033
5th rowSG0036
ValueCountFrequency (%)
cp2800 3
 
6.1%
sg0036 2
 
4.1%
kr0002 2
 
4.1%
us1254 2
 
4.1%
kr0049 2
 
4.1%
sn0003 2
 
4.1%
za0033 2
 
4.1%
co0007 1
 
2.0%
gb0302 1
 
2.0%
il0008 1
 
2.0%
Other values (31) 31
63.3%
2023-12-11T00:00:01.836708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 91
31.0%
3 19
 
6.5%
1 16
 
5.4%
5 14
 
4.8%
S 14
 
4.8%
2 13
 
4.4%
C 12
 
4.1%
A 12
 
4.1%
8 11
 
3.7%
7 9
 
3.1%
Other values (20) 83
28.2%

Most occurring categories

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

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 14
14.3%
C 12
12.2%
A 12
12.2%
U 8
 
8.2%
P 7
 
7.1%
N 7
 
7.1%
R 5
 
5.1%
G 5
 
5.1%
K 4
 
4.1%
B 4
 
4.1%
Other values (10) 20
20.4%
Decimal Number
ValueCountFrequency (%)
0 91
46.4%
3 19
 
9.7%
1 16
 
8.2%
5 14
 
7.1%
2 13
 
6.6%
8 11
 
5.6%
7 9
 
4.6%
6 9
 
4.6%
4 8
 
4.1%
9 6
 
3.1%

Most occurring scripts

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

Most frequent character per script

Latin
ValueCountFrequency (%)
S 14
14.3%
C 12
12.2%
A 12
12.2%
U 8
 
8.2%
P 7
 
7.1%
N 7
 
7.1%
R 5
 
5.1%
G 5
 
5.1%
K 4
 
4.1%
B 4
 
4.1%
Other values (10) 20
20.4%
Common
ValueCountFrequency (%)
0 91
46.4%
3 19
 
9.7%
1 16
 
8.2%
5 14
 
7.1%
2 13
 
6.6%
8 11
 
5.6%
7 9
 
4.6%
6 9
 
4.6%
4 8
 
4.1%
9 6
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91
31.0%
3 19
 
6.5%
1 16
 
5.4%
5 14
 
4.8%
S 14
 
4.8%
2 13
 
4.4%
C 12
 
4.1%
A 12
 
4.1%
8 11
 
3.7%
7 9
 
3.1%
Other values (20) 83
28.2%

ARRV_PRT_CD
Text

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-11T00:00:02.351772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters294
Distinct characters32
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 rowSG0045
2nd rowJP0274
3rd rowJP0190
4th rowSG0036
5th rowMX0039
ValueCountFrequency (%)
mx0039 3
 
6.1%
us0994 2
 
4.1%
jp0310 2
 
4.1%
ae0080 2
 
4.1%
eg0053 2
 
4.1%
cp2800 2
 
4.1%
cn0540 1
 
2.0%
ma0048 1
 
2.0%
mx0105 1
 
2.0%
sg0050 1
 
2.0%
Other values (32) 32
65.3%
2023-12-11T00:00:03.547401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 97
33.0%
9 16
 
5.4%
3 14
 
4.8%
2 14
 
4.8%
1 11
 
3.7%
S 11
 
3.7%
4 11
 
3.7%
G 10
 
3.4%
6 10
 
3.4%
E 10
 
3.4%
Other values (22) 90
30.6%

Most occurring categories

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

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 11
11.2%
G 10
 
10.2%
E 10
 
10.2%
C 8
 
8.2%
M 7
 
7.1%
P 7
 
7.1%
U 6
 
6.1%
A 5
 
5.1%
J 5
 
5.1%
X 4
 
4.1%
Other values (12) 25
25.5%
Decimal Number
ValueCountFrequency (%)
0 97
49.5%
9 16
 
8.2%
3 14
 
7.1%
2 14
 
7.1%
1 11
 
5.6%
4 11
 
5.6%
6 10
 
5.1%
8 9
 
4.6%
5 9
 
4.6%
7 5
 
2.6%

Most occurring scripts

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

Most frequent character per script

Latin
ValueCountFrequency (%)
S 11
11.2%
G 10
 
10.2%
E 10
 
10.2%
C 8
 
8.2%
M 7
 
7.1%
P 7
 
7.1%
U 6
 
6.1%
A 5
 
5.1%
J 5
 
5.1%
X 4
 
4.1%
Other values (12) 25
25.5%
Common
ValueCountFrequency (%)
0 97
49.5%
9 16
 
8.2%
3 14
 
7.1%
2 14
 
7.1%
1 11
 
5.6%
4 11
 
5.6%
6 10
 
5.1%
8 9
 
4.6%
5 9
 
4.6%
7 5
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97
33.0%
9 16
 
5.4%
3 14
 
4.8%
2 14
 
4.8%
1 11
 
3.7%
S 11
 
3.7%
4 11
 
3.7%
G 10
 
3.4%
6 10
 
3.4%
E 10
 
3.4%
Other values (22) 90
30.6%

DPTR_PRT_NM
Text

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-11T00:00:04.203593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length33
Mean length19.857143
Min length4

Characters and Unicode

Total characters973
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)69.4%

Sample

1st rowLyme Bay TSA
2nd rowBusan
3rd rowGuam
4th rowCape Town Container Terminal
5th rowSingapore
ValueCountFrequency (%)
14
 
9.5%
terminal 11
 
7.4%
container 8
 
5.4%
port 7
 
4.7%
canal 4
 
2.7%
entrance 4
 
2.7%
singapore 4
 
2.7%
said 3
 
2.0%
pusan 2
 
1.4%
tanjung 2
 
1.4%
Other values (80) 89
60.1%
2023-12-11T00:00:05.152141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 129
 
13.3%
99
 
10.2%
n 86
 
8.8%
e 73
 
7.5%
r 72
 
7.4%
i 51
 
5.2%
o 44
 
4.5%
l 36
 
3.7%
t 31
 
3.2%
d 23
 
2.4%
Other values (43) 329
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 697
71.6%
Uppercase Letter 141
 
14.5%
Space Separator 99
 
10.2%
Dash Punctuation 14
 
1.4%
Other Punctuation 14
 
1.4%
Close Punctuation 4
 
0.4%
Open Punctuation 4
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 129
18.5%
n 86
12.3%
e 73
10.5%
r 72
10.3%
i 51
 
7.3%
o 44
 
6.3%
l 36
 
5.2%
t 31
 
4.4%
d 23
 
3.3%
u 22
 
3.2%
Other values (15) 130
18.7%
Uppercase Letter
ValueCountFrequency (%)
T 21
14.9%
C 19
13.5%
P 16
11.3%
B 15
10.6%
S 10
 
7.1%
M 8
 
5.7%
E 7
 
5.0%
D 5
 
3.5%
G 4
 
2.8%
A 4
 
2.8%
Other values (12) 32
22.7%
Other Punctuation
ValueCountFrequency (%)
. 9
64.3%
, 5
35.7%
Space Separator
ValueCountFrequency (%)
99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 838
86.1%
Common 135
 
13.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 129
15.4%
n 86
 
10.3%
e 73
 
8.7%
r 72
 
8.6%
i 51
 
6.1%
o 44
 
5.3%
l 36
 
4.3%
t 31
 
3.7%
d 23
 
2.7%
u 22
 
2.6%
Other values (37) 271
32.3%
Common
ValueCountFrequency (%)
99
73.3%
- 14
 
10.4%
. 9
 
6.7%
, 5
 
3.7%
) 4
 
3.0%
( 4
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 129
 
13.3%
99
 
10.2%
n 86
 
8.8%
e 73
 
7.5%
r 72
 
7.4%
i 51
 
5.2%
o 44
 
4.5%
l 36
 
3.7%
t 31
 
3.2%
d 23
 
2.4%
Other values (43) 329
33.8%

ARVL_PRT_NM
Text

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-11T00:00:05.767909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length28
Mean length18.918367
Min length4

Characters and Unicode

Total characters927
Distinct characters54
Distinct categories7 ?
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 rowEastern Boarding Ground B - Singapore
2nd rowShimizu, (Honshu), Japan
3rd rowNaha
4th rowSingapore
5th rowManzanillo, Mexico
ValueCountFrequency (%)
11
 
7.9%
terminal 11
 
7.9%
port 8
 
5.7%
container 6
 
4.3%
manzanillo 3
 
2.1%
mexico 3
 
2.1%
singapore 3
 
2.1%
tsa 3
 
2.1%
ain 2
 
1.4%
eastern 2
 
1.4%
Other values (73) 88
62.9%
2023-12-11T00:00:06.815874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 97
 
10.5%
91
 
9.8%
n 87
 
9.4%
e 68
 
7.3%
o 66
 
7.1%
i 58
 
6.3%
r 57
 
6.1%
l 36
 
3.9%
t 36
 
3.9%
h 24
 
2.6%
Other values (44) 307
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 672
72.5%
Uppercase Letter 133
 
14.3%
Space Separator 91
 
9.8%
Dash Punctuation 12
 
1.3%
Other Punctuation 9
 
1.0%
Close Punctuation 5
 
0.5%
Open Punctuation 5
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 97
14.4%
n 87
12.9%
e 68
10.1%
o 66
9.8%
i 58
8.6%
r 57
8.5%
l 36
 
5.4%
t 36
 
5.4%
h 24
 
3.6%
u 20
 
3.0%
Other values (16) 123
18.3%
Uppercase Letter
ValueCountFrequency (%)
T 21
15.8%
C 15
11.3%
S 12
9.0%
M 11
 
8.3%
P 11
 
8.3%
G 8
 
6.0%
A 8
 
6.0%
N 7
 
5.3%
B 6
 
4.5%
E 6
 
4.5%
Other values (12) 28
21.1%
Other Punctuation
ValueCountFrequency (%)
, 8
88.9%
. 1
 
11.1%
Space Separator
ValueCountFrequency (%)
91
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 805
86.8%
Common 122
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 97
 
12.0%
n 87
 
10.8%
e 68
 
8.4%
o 66
 
8.2%
i 58
 
7.2%
r 57
 
7.1%
l 36
 
4.5%
t 36
 
4.5%
h 24
 
3.0%
T 21
 
2.6%
Other values (38) 255
31.7%
Common
ValueCountFrequency (%)
91
74.6%
- 12
 
9.8%
, 8
 
6.6%
) 5
 
4.1%
( 5
 
4.1%
. 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 97
 
10.5%
91
 
9.8%
n 87
 
9.4%
e 68
 
7.3%
o 66
 
7.1%
i 58
 
6.3%
r 57
 
6.1%
l 36
 
3.9%
t 36
 
3.9%
h 24
 
2.6%
Other values (44) 307
33.1%

SHIP_CNT
Real number (ℝ)

Distinct16
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5102041
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-11T00:00:07.185830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile23.6
Maximum38
Range37
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.2136128
Coefficient of variation (CV)1.2616521
Kurtosis5.2730514
Mean6.5102041
Median Absolute Deviation (MAD)2
Skewness2.3087893
Sum319
Variance67.463435
MonotonicityNot monotonic
2023-12-11T00:00:07.665698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 10
20.4%
2 8
16.3%
3 8
16.3%
4 6
12.2%
5 3
 
6.1%
11 2
 
4.1%
9 2
 
4.1%
6 2
 
4.1%
38 1
 
2.0%
14 1
 
2.0%
Other values (6) 6
12.2%
ValueCountFrequency (%)
1 10
20.4%
2 8
16.3%
3 8
16.3%
4 6
12.2%
5 3
 
6.1%
6 2
 
4.1%
9 2
 
4.1%
10 1
 
2.0%
11 2
 
4.1%
14 1
 
2.0%
ValueCountFrequency (%)
38 1
2.0%
32 1
2.0%
24 1
2.0%
23 1
2.0%
19 1
2.0%
18 1
2.0%
14 1
2.0%
11 2
4.1%
10 1
2.0%
9 2
4.1%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 14:58:27
Maximum2023-04-10 03:16:48
2023-12-11T00:00:08.159497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:08.620883image/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
Minimum2023-01-26 04:01:58
Maximum2023-04-30 23:59:17
2023-12-11T00:00:08.923359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:09.207712image/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%
Mean1.5481398 × 108
Minimum1.49626 × 108
Maximum1.59528 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-11T00:00:09.663182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.49626 × 108
5-th percentile1.502214 × 108
Q11.52166 × 108
median1.54383 × 108
Q31.58037 × 108
95-th percentile1.590738 × 108
Maximum1.59528 × 108
Range9902000
Interquartile range (IQR)5871000

Descriptive statistics

Standard deviation3105601.1
Coefficient of variation (CV)0.020060211
Kurtosis-1.323595
Mean1.5481398 × 108
Median Absolute Deviation (MAD)2934000
Skewness-0.0081775475
Sum7.585885 × 109
Variance9.6447582 × 1012
MonotonicityStrictly decreasing
2023-12-11T00:00:10.089425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
159528000 1
 
2.0%
151802000 1
 
2.0%
154054000 1
 
2.0%
153717000 1
 
2.0%
153616000 1
 
2.0%
153529000 1
 
2.0%
153310000 1
 
2.0%
153197000 1
 
2.0%
153057000 1
 
2.0%
153036000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
149626000 1
2.0%
149914000 1
2.0%
150015000 1
2.0%
150531000 1
2.0%
150663000 1
2.0%
150770000 1
2.0%
150917000 1
2.0%
150939000 1
2.0%
151051000 1
2.0%
151449000 1
2.0%
ValueCountFrequency (%)
159528000 1
2.0%
159517000 1
2.0%
159191000 1
2.0%
158898000 1
2.0%
158887000 1
2.0%
158882000 1
2.0%
158852000 1
2.0%
158752000 1
2.0%
158625000 1
2.0%
158329000 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
2023-12-11T00:00:10.518754image/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
2023-12-11T00:00:11.100043image/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

2023-12-10T23:59:55.775944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:52.670864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:54.005550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:54.943593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:55.998987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:53.178970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:54.337988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:55.128118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.164013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:53.502530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:54.542001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:55.369293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.375831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:53.786233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:54.748313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:55.567867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:00:11.336654image/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
RANK1.0000.3000.4940.0000.8640.0000.8640.4581.0001.0000.9771.000
DPTR_CN_NM0.3001.0000.7441.0000.9621.0000.9620.0001.0001.0000.3010.538
ARVL_CN_NM0.4940.7441.0000.9451.0000.9451.0000.6751.0001.0000.8000.614
DPRT_PRT_CD0.0001.0000.9451.0000.9091.0000.9090.0001.0001.0000.7750.357
ARRV_PRT_CD0.8640.9621.0000.9091.0000.9091.0000.9381.0001.0000.9100.847
DPTR_PRT_NM0.0001.0000.9451.0000.9091.0000.9090.0001.0001.0000.7750.357
ARVL_PRT_NM0.8640.9621.0000.9091.0000.9091.0000.9381.0001.0000.9100.847
SHIP_CNT0.4580.0000.6750.0000.9380.0000.9381.0001.0001.0000.3880.433
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_QTY0.9770.3010.8000.7750.9100.7750.9100.3881.0001.0001.0000.982
RN1.0000.5380.6140.3570.8470.3570.8470.4331.0001.0000.9821.000
2023-12-11T00:00:11.633690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRN
RANK1.0000.054-1.0001.000
SHIP_CNT0.0541.000-0.0540.054
FRGHT_CNVNC_QTY-1.000-0.0541.000-1.000
RN1.0000.054-1.0001.000

Missing values

2023-12-10T23:59:56.660530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:59:57.049294image/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
0469United KingdomSingaporeGB0302SG0045Lyme Bay TSAEastern Boarding Ground B - Singapore117-Jan-2023 19:01:4817-Mar-2023 06:10:081595280002
1470South KoreaJapanKR0002JP0274BusanShimizu, (Honshu), Japan3802-Jan-2023 19:51:3927-Apr-2023 10:16:341595170003
2471Northern Marinana Islands-GuamJapanUP0001JP0190GuamNaha1404-Jan-2023 20:44:2728-Apr-2023 23:52:491591910004
3472South AfricaSingaporeZA0033SG0036Cape Town Container TerminalSingapore326-Jan-2023 13:10:0522-Apr-2023 01:16:161588980005
4473SingaporeMexicoSG0036MX0039SingaporeManzanillo, Mexico126-Jan-2023 22:57:5502-Apr-2023 00:47:031588870006
5474AustraliaIndonesiaAU0439ID0302Adelaide - Container TerminalTanjung Priok518-Jan-2023 19:31:1213-Apr-2023 20:44:391588820007
6475EgyptUnited StatesCP2800US0650Port Said - Canal EntrancePort Wentworth121-Jan-2023 04:38:1702-Apr-2023 09:12:441588520008
7476BelgiumSpainBE0028ES0157Antwerp - DeurganckdokValencia605-Jan-2023 09:44:2025-Apr-2023 10:10:571587520009
8477AustraliaMalaysiaAU0038MY0182BrisbaneTanjung Bin409-Jan-2023 17:56:0827-Feb-2023 05:08:4115862500010
9478CanadaGermanyCA0708DE0014Halifax - Ocean TerminalsBremerhaven503-Jan-2023 00:18:0317-Apr-2023 13:47:3615832900011
RANKDPTR_CN_NMARVL_CN_NMDPRT_PRT_CDARRV_PRT_CDDPTR_PRT_NMARVL_PRT_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
39508SpainUnited StatesES0198US0994Isla VerdePort Elizabeth Marine Terminal406-Jan-2023 00:59:0605-Apr-2023 19:17:1615144900041
40509The BahamasUnited StatesBS0006US0994Freeport, (Grand Bahama Is.), BahamasPort Elizabeth Marine Terminal301-Jan-2023 14:58:2708-Apr-2023 07:53:1315105100042
41510MexicoTaiwanMX0039TW0006Manzanillo, MexicoKaohsiung226-Jan-2023 19:01:5924-Feb-2023 06:32:5315093900043
42511CanadaUnited StatesCA0724US0796Prince Rupert - Fairview Container TerminalTacoma406-Jan-2023 17:21:1623-Apr-2023 12:08:3815091700044
43512United StatesSouth KoreaUS0728KR0049SeattlePusan New Port303-Jan-2023 11:24:1925-Apr-2023 01:56:2315077000045
44513PanamaEcuadorCP1550EC0008Balboa (Pacific Entrance To Panama Canal)Guayaquil2402-Jan-2023 13:34:0629-Apr-2023 06:00:5515066300046
45514GermanyUnited StatesDE0014US1022BremerhavenCooper River - North Charleston Terminal312-Jan-2023 17:34:5824-Feb-2023 14:14:5615053100047
46515South KoreaMexicoKR0049MX0105Pusan New PortCosta Azul LNG Terminal205-Jan-2023 06:14:2726-Jan-2023 04:01:5815001500048
47516ChinaSouth KoreaCN0554KR0072Xiamen - Xiangyu Tariff Free Zone WharfInchon - Soraepogu LNG Terminal1903-Jan-2023 21:51:4929-Apr-2023 15:37:0914991400049
48517MalaysiaEgyptMY0182EG0053Tanjung BinOffshore Ain Sukhna (Tsa)212-Jan-2023 08:31:5321-Apr-2023 04:02:3314962600050