Overview

Dataset statistics

Number of variables7
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory62.7 B

Variable types

Numeric4
Text1
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001076

Alerts

RANK is highly overall correlated with SHIP_CNT and 2 other fieldsHigh correlation
SHIP_CNT is highly overall correlated with RANK and 2 other fieldsHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with RANK and 2 other fieldsHigh correlation
RN is highly overall correlated with RANK and 2 other fieldsHigh correlation
RANK has unique valuesUnique
SHIP_CNT has unique valuesUnique
FRGHT_CNVNC_QTY has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:40:30.807967
Analysis finished2023-12-10 14:40:32.324541
Duration1.52 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%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:40:32.385993image/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
2023-12-10T23:40:32.501404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

ARVL_CN_NM
Text

Distinct32
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:40:32.686293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.6938776
Min length4

Characters and Unicode

Total characters377
Distinct characters42
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

Unique24 ?
Unique (%)49.0%

Sample

1st rowSouth Africa
2nd rowChina
3rd rowYemen
4th rowSri Lanka
5th rowTaiwan
ValueCountFrequency (%)
spain 5
 
8.5%
china 5
 
8.5%
united 3
 
5.1%
states 3
 
5.1%
indonesia 3
 
5.1%
south 3
 
5.1%
africa 3
 
5.1%
morocco 2
 
3.4%
malaysia 2
 
3.4%
france 2
 
3.4%
Other values (28) 28
47.5%
2023-12-10T23:40:32.972318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 59
15.6%
i 40
 
10.6%
n 35
 
9.3%
e 25
 
6.6%
r 20
 
5.3%
t 20
 
5.3%
S 14
 
3.7%
s 14
 
3.7%
u 14
 
3.7%
o 13
 
3.4%
Other values (32) 123
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 308
81.7%
Uppercase Letter 59
 
15.6%
Space Separator 10
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 59
19.2%
i 40
13.0%
n 35
11.4%
e 25
8.1%
r 20
 
6.5%
t 20
 
6.5%
s 14
 
4.5%
u 14
 
4.5%
o 13
 
4.2%
p 10
 
3.2%
Other values (14) 58
18.8%
Uppercase Letter
ValueCountFrequency (%)
S 14
23.7%
M 7
11.9%
A 6
10.2%
C 5
 
8.5%
I 4
 
6.8%
U 4
 
6.8%
P 4
 
6.8%
L 2
 
3.4%
N 2
 
3.4%
G 2
 
3.4%
Other values (7) 9
15.3%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 367
97.3%
Common 10
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 59
16.1%
i 40
 
10.9%
n 35
 
9.5%
e 25
 
6.8%
r 20
 
5.4%
t 20
 
5.4%
S 14
 
3.8%
s 14
 
3.8%
u 14
 
3.8%
o 13
 
3.5%
Other values (31) 113
30.8%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 59
15.6%
i 40
 
10.6%
n 35
 
9.3%
e 25
 
6.6%
r 20
 
5.3%
t 20
 
5.3%
S 14
 
3.7%
s 14
 
3.7%
u 14
 
3.7%
o 13
 
3.4%
Other values (32) 123
32.6%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2331.8776
Minimum95
Maximum9083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:40:33.118465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile173.8
Q1576
median1977
Q33015
95-th percentile6463.6
Maximum9083
Range8988
Interquartile range (IQR)2439

Descriptive statistics

Standard deviation2057.1807
Coefficient of variation (CV)0.88219929
Kurtosis1.9850183
Mean2331.8776
Median Absolute Deviation (MAD)1295
Skewness1.3697038
Sum114262
Variance4231992.5
MonotonicityNot monotonic
2023-12-10T23:40:33.239434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3035 1
 
2.0%
507 1
 
2.0%
4712 1
 
2.0%
3015 1
 
2.0%
571 1
 
2.0%
2618 1
 
2.0%
315 1
 
2.0%
1770 1
 
2.0%
312 1
 
2.0%
947 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
95 1
2.0%
109 1
2.0%
167 1
2.0%
184 1
2.0%
247 1
2.0%
252 1
2.0%
312 1
2.0%
315 1
2.0%
409 1
2.0%
440 1
2.0%
ValueCountFrequency (%)
9083 1
2.0%
7703 1
2.0%
6896 1
2.0%
5815 1
2.0%
5682 1
2.0%
4712 1
2.0%
4627 1
2.0%
3906 1
2.0%
3622 1
2.0%
3434 1
2.0%

DPTR_HMS
Date

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:02
Maximum2022-01-01 00:59:26
2023-12-10T23:40:33.349066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:33.454566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)

ARVL_HMS
Date

Distinct34
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-07-17 21:41:47
Maximum2022-07-17 22:00:21
2023-12-10T23:40:33.554499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:33.658619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.125178 × 1011
Minimum2.04917 × 1010
Maximum1.16206 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:40:33.762425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.04917 × 1010
5-th percentile2.205526 × 1010
Q13.66713 × 1010
median8.34907 × 1010
Q32.07442 × 1011
95-th percentile9.221236 × 1011
Maximum1.16206 × 1012
Range1.1415683 × 1012
Interquartile range (IQR)1.707707 × 1011

Descriptive statistics

Standard deviation2.9237704 × 1011
Coefficient of variation (CV)1.3757767
Kurtosis4.059142
Mean2.125178 × 1011
Median Absolute Deviation (MAD)5.90587 × 1010
Skewness2.1778732
Sum1.0413372 × 1013
Variance8.5484333 × 1022
MonotonicityStrictly decreasing
2023-12-10T23:40:34.129289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1162060000000 1
 
2.0%
36353400000 1
 
2.0%
74500000000 1
 
2.0%
65476600000 1
 
2.0%
64406800000 1
 
2.0%
64353400000 1
 
2.0%
61430400000 1
 
2.0%
58667400000 1
 
2.0%
52236800000 1
 
2.0%
46307400000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
20491700000 1
2.0%
20999500000 1
2.0%
21689300000 1
2.0%
22604200000 1
2.0%
23758500000 1
2.0%
24432000000 1
2.0%
26800600000 1
2.0%
27711200000 1
2.0%
31010600000 1
2.0%
34014100000 1
2.0%
ValueCountFrequency (%)
1162060000000 1
2.0%
1155960000000 1
2.0%
939892000000 1
2.0%
895471000000 1
2.0%
791298000000 1
2.0%
463598000000 1
2.0%
451866000000 1
2.0%
400696000000 1
2.0%
347457000000 1
2.0%
327960000000 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
2023-12-10T23:40:34.248870image/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
2023-12-10T23:40:34.364300image/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

2023-12-10T23:40:31.908459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.083932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.340153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.601522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.972394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.146593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.403428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.674141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:32.036439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.207446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.459444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.742084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:32.106097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.278261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.530168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:31.824345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:40:34.442207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKARVL_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
RANK1.0000.5660.5510.5400.7650.7551.000
ARVL_CN_NM0.5661.0000.7840.0000.8640.8810.566
SHIP_CNT0.5510.7841.0000.0000.0000.8620.551
DPTR_HMS0.5400.0000.0001.0000.9230.0000.540
ARVL_HMS0.7650.8640.0000.9231.0000.0000.765
FRGHT_CNVNC_QTY0.7550.8810.8620.0000.0001.0000.755
RN1.0000.5660.5510.5400.7650.7551.000
2023-12-10T23:40:34.542468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRN
RANK1.000-0.778-1.0001.000
SHIP_CNT-0.7781.0000.778-0.778
FRGHT_CNVNC_QTY-1.0000.7781.000-1.000
RN1.000-0.778-1.0001.000

Missing values

2023-12-10T23:40:32.193667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:40:32.285599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RANKARVL_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
02South Africa303501-Jan-2022 00:01:4517-Jul-2022 22:00:1811620600000002
13China908301-Jan-2022 00:00:2517-Jul-2022 22:00:1811559600000003
24Yemen271001-Jan-2022 00:00:0217-Jul-2022 22:00:199398920000004
35Sri Lanka689601-Jan-2022 00:01:4317-Jul-2022 21:59:598954710000005
46Taiwan770301-Jan-2022 00:04:5117-Jul-2022 22:00:187912980000006
57China343401-Jan-2022 00:02:5517-Jul-2022 22:00:184635980000007
68Papua New Guinea462701-Jan-2022 00:00:2117-Jul-2022 22:00:114518660000008
79Mexico568201-Jan-2022 00:00:3317-Jul-2022 22:00:154006960000009
810China390601-Jan-2022 00:01:1017-Jul-2022 21:59:4434745700000010
911Morocco581501-Jan-2022 00:00:1517-Jul-2022 21:59:3932796000000011
RANKARVL_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
3941China24701-Jan-2022 00:02:5817-Jul-2022 21:59:343401410000041
4042Mauritania40901-Jan-2022 00:59:2617-Jul-2022 21:59:393101060000042
4143France103401-Jan-2022 00:02:4817-Jul-2022 21:55:452771120000043
4244France25201-Jan-2022 00:02:0817-Jul-2022 21:58:222680060000044
4345United States75201-Jan-2022 00:03:5817-Jul-2022 21:58:222443200000045
4446South Africa9501-Jan-2022 00:05:3517-Jul-2022 21:55:062375850000046
4547Germany197701-Jan-2022 00:00:2017-Jul-2022 21:58:332260420000047
4648United States18401-Jan-2022 00:41:2617-Jul-2022 22:00:072168930000048
4749Indonesia10901-Jan-2022 00:01:1617-Jul-2022 21:56:022099950000049
4850Spain16701-Jan-2022 00:02:5517-Jul-2022 21:52:502049170000050