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

Numeric3
Text1
Categorical3

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001099

Alerts

RANK is highly overall correlated with CDBX and 1 other fieldsHigh correlation
CDBX is highly overall correlated with RANK and 1 other fieldsHigh correlation
RN is highly overall correlated with RANK and 1 other fieldsHigh correlation
SHIP_CNT is highly overall correlated with DPTR_HMSHigh correlation
DPTR_HMS is highly overall correlated with SHIP_CNT and 1 other fieldsHigh correlation
ARVL_HMS is highly overall correlated with DPTR_HMSHigh correlation
DPTR_HMS is highly imbalanced (66.9%)Imbalance
ARVL_HMS is highly imbalanced (54.2%)Imbalance
RANK has unique valuesUnique
SHIP_OWNER_NM has unique valuesUnique
CDBX has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:43:07.476405
Analysis finished2023-12-10 14:43:08.559951
Duration1.08 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%
Mean584
Minimum560
Maximum608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:08.642079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum560
5-th percentile562.4
Q1572
median584
Q3596
95-th percentile605.6
Maximum608
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.024466935
Kurtosis-1.2
Mean584
Median Absolute Deviation (MAD)12
Skewness0
Sum28616
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:43:08.759440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
560 1
 
2.0%
597 1
 
2.0%
587 1
 
2.0%
588 1
 
2.0%
589 1
 
2.0%
590 1
 
2.0%
591 1
 
2.0%
592 1
 
2.0%
593 1
 
2.0%
594 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
560 1
2.0%
561 1
2.0%
562 1
2.0%
563 1
2.0%
564 1
2.0%
565 1
2.0%
566 1
2.0%
567 1
2.0%
568 1
2.0%
569 1
2.0%
ValueCountFrequency (%)
608 1
2.0%
607 1
2.0%
606 1
2.0%
605 1
2.0%
604 1
2.0%
603 1
2.0%
602 1
2.0%
601 1
2.0%
600 1
2.0%
599 1
2.0%

SHIP_OWNER_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:43:08.946694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length13
Min length5

Characters and Unicode

Total characters637
Distinct characters49
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st rowDqeurvPdulwlphVD
2nd rowVhdohvwldoQdyFr
3rd rowSroirufhVksj
4th rowFGSurvshu
5th rowXqlyhuvdoWdqnhu
ValueCountFrequency (%)
dqeurvpdulwlphvd 1
 
2.0%
qlqjerfkxqwdl 1
 
2.0%
wxuerwdfw 1
 
2.0%
ckhmldqjzhltlq 1
 
2.0%
krqjalqvksj 1
 
2.0%
frvprvklspdu 1
 
2.0%
wdlvklsghyhorsphqw 1
 
2.0%
dqjorvzlvvpdulwlph 1
 
2.0%
ylydvklsslqj 1
 
2.0%
qreohjurxs 1
 
2.0%
Other values (39) 39
79.6%
2023-12-10T23:43:09.521716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 69
 
10.8%
q 63
 
9.9%
d 51
 
8.0%
h 45
 
7.1%
r 38
 
6.0%
k 28
 
4.4%
w 27
 
4.2%
u 25
 
3.9%
V 25
 
3.9%
j 24
 
3.8%
Other values (39) 242
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 508
79.7%
Uppercase Letter 129
 
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 69
13.6%
q 63
12.4%
d 51
10.0%
h 45
 
8.9%
r 38
 
7.5%
k 28
 
5.5%
w 27
 
5.3%
u 25
 
4.9%
j 24
 
4.7%
s 21
 
4.1%
Other values (16) 117
23.0%
Uppercase Letter
ValueCountFrequency (%)
V 25
19.4%
P 11
 
8.5%
K 9
 
7.0%
F 9
 
7.0%
L 7
 
5.4%
N 7
 
5.4%
W 7
 
5.4%
C 5
 
3.9%
J 5
 
3.9%
H 5
 
3.9%
Other values (13) 39
30.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 637
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 69
 
10.8%
q 63
 
9.9%
d 51
 
8.0%
h 45
 
7.1%
r 38
 
6.0%
k 28
 
4.4%
w 27
 
4.2%
u 25
 
3.9%
V 25
 
3.9%
j 24
 
3.8%
Other values (39) 242
38.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 69
 
10.8%
q 63
 
9.9%
d 51
 
8.0%
h 45
 
7.1%
r 38
 
6.0%
k 28
 
4.4%
w 27
 
4.2%
u 25
 
3.9%
V 25
 
3.9%
j 24
 
3.8%
Other values (39) 242
38.0%

SHIP_CNT
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
1
41 
2
8 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 41
83.7%
2 8
 
16.3%

Length

2023-12-10T23:43:09.644627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:43:09.721724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 41
83.7%
2 8
 
16.3%

DPTR_HMS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2023 12:00:00
42 
20-Feb-2023 18:00:00
 
1
01-Jan-2023 06:00:00
 
1
22-Jan-2023 12:00:00
 
1
01-Jan-2023 18:00:00
 
1
Other values (3)
 
3

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique7 ?
Unique (%)14.3%

Sample

1st row01-Jan-2023 12:00:00
2nd row01-Jan-2023 12:00:00
3rd row01-Jan-2023 12:00:00
4th row01-Jan-2023 12:00:00
5th row01-Jan-2023 12:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2023 12:00:00 42
85.7%
20-Feb-2023 18:00:00 1
 
2.0%
01-Jan-2023 06:00:00 1
 
2.0%
22-Jan-2023 12:00:00 1
 
2.0%
01-Jan-2023 18:00:00 1
 
2.0%
24-Apr-2023 00:00:00 1
 
2.0%
02-Jan-2023 00:00:00 1
 
2.0%
25-Apr-2023 00:00:00 1
 
2.0%

Length

2023-12-10T23:43:09.801842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:43:09.893812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
01-jan-2023 44
44.9%
12:00:00 43
43.9%
00:00:00 3
 
3.1%
18:00:00 2
 
2.0%
20-feb-2023 1
 
1.0%
06:00:00 1
 
1.0%
22-jan-2023 1
 
1.0%
24-apr-2023 1
 
1.0%
02-jan-2023 1
 
1.0%
25-apr-2023 1
 
1.0%

ARVL_HMS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
31-May-2023 18:00:00
37 
29-May-2023 18:00:00
 
2
25-May-2023 18:00:00
 
1
29-May-2023 00:00:00
 
1
22-May-2023 12:00:00
 
1
Other values (7)
7 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique10 ?
Unique (%)20.4%

Sample

1st row31-May-2023 18:00:00
2nd row25-May-2023 18:00:00
3rd row31-May-2023 18:00:00
4th row31-May-2023 18:00:00
5th row31-May-2023 18:00:00

Common Values

ValueCountFrequency (%)
31-May-2023 18:00:00 37
75.5%
29-May-2023 18:00:00 2
 
4.1%
25-May-2023 18:00:00 1
 
2.0%
29-May-2023 00:00:00 1
 
2.0%
22-May-2023 12:00:00 1
 
2.0%
27-May-2023 18:00:00 1
 
2.0%
21-Apr-2023 00:00:00 1
 
2.0%
27-May-2023 00:00:00 1
 
2.0%
21-May-2023 18:00:00 1
 
2.0%
03-Apr-2023 00:00:00 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2023-12-10T23:43:10.000798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18:00:00 42
42.9%
31-may-2023 38
38.8%
00:00:00 5
 
5.1%
29-may-2023 3
 
3.1%
12:00:00 2
 
2.0%
27-may-2023 2
 
2.0%
25-may-2023 1
 
1.0%
22-may-2023 1
 
1.0%
21-apr-2023 1
 
1.0%
21-may-2023 1
 
1.0%
Other values (2) 2
 
2.0%

CDBX
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.232539 × 109
Minimum5.66234 × 109
Maximum6.85753 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:10.105504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.66234 × 109
5-th percentile5.688982 × 109
Q15.83157 × 109
median6.21757 × 109
Q36.56129 × 109
95-th percentile6.839374 × 109
Maximum6.85753 × 109
Range1.19519 × 109
Interquartile range (IQR)7.2972 × 108

Descriptive statistics

Standard deviation4.0201193 × 108
Coefficient of variation (CV)0.064502112
Kurtosis-1.3810196
Mean6.232539 × 109
Median Absolute Deviation (MAD)3.8273 × 108
Skewness0.052116383
Sum3.0539441 × 1011
Variance1.6161359 × 1017
MonotonicityStrictly decreasing
2023-12-10T23:43:10.266338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
6857530000 1
 
2.0%
5784140000 1
 
2.0%
6134400000 1
 
2.0%
6128290000 1
 
2.0%
6106630000 1
 
2.0%
6032120000 1
 
2.0%
6031030000 1
 
2.0%
5964660000 1
 
2.0%
5936350000 1
 
2.0%
5932150000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
5662340000 1
2.0%
5679040000 1
2.0%
5683790000 1
2.0%
5696770000 1
2.0%
5705730000 1
2.0%
5710050000 1
2.0%
5736350000 1
2.0%
5738750000 1
2.0%
5744480000 1
2.0%
5751010000 1
2.0%
ValueCountFrequency (%)
6857530000 1
2.0%
6846800000 1
2.0%
6840450000 1
2.0%
6837760000 1
2.0%
6789450000 1
2.0%
6786690000 1
2.0%
6783610000 1
2.0%
6783360000 1
2.0%
6674470000 1
2.0%
6656620000 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
2023-12-10T23:43:10.395719image/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
2023-12-10T23:43:10.632586image/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

2023-12-10T23:43:08.170339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:07.750524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:07.949590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:08.242566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:07.814851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:08.016243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:08.319561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:07.884027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:08.097613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:43:10.729856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
RANK1.0001.0000.0000.0960.0000.9761.000
SHIP_OWNER_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.0001.0001.0000.8920.7070.0000.000
DPTR_HMS0.0961.0000.8921.0000.8680.2370.178
ARVL_HMS0.0001.0000.7070.8681.0000.0000.171
CDBX0.9761.0000.0000.2370.0001.0000.982
RN1.0001.0000.0000.1780.1710.9821.000
2023-12-10T23:43:10.835073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTDPTR_HMSARVL_HMS
SHIP_CNT1.0000.6690.493
DPTR_HMS0.6691.0000.564
ARVL_HMS0.4930.5641.000
2023-12-10T23:43:10.927194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKCDBXRNSHIP_CNTDPTR_HMSARVL_HMS
RANK1.000-1.0001.0000.0000.0500.000
CDBX-1.0001.000-1.0000.0000.1000.000
RN1.000-1.0001.0000.0000.0500.000
SHIP_CNT0.0000.0000.0001.0000.6690.493
DPTR_HMS0.0500.1000.0500.6691.0000.564
ARVL_HMS0.0000.0000.0000.4930.5641.000

Missing values

2023-12-10T23:43:08.420064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:43:08.519223image/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

RANKSHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
0560DqeurvPdulwlphVD101-Jan-2023 12:00:0031-May-2023 18:00:0068575300002
1561VhdohvwldoQdyFr101-Jan-2023 12:00:0025-May-2023 18:00:0068468000003
2562SroirufhVksj101-Jan-2023 12:00:0031-May-2023 18:00:0068404500004
3563FGSurvshu101-Jan-2023 12:00:0031-May-2023 18:00:0068377600005
4564XqlyhuvdoWdqnhu101-Jan-2023 12:00:0031-May-2023 18:00:0067894500006
5565WrnrXqbxNN101-Jan-2023 12:00:0031-May-2023 18:00:0067866900007
6566Hqhwl101-Jan-2023 12:00:0031-May-2023 18:00:0067836100008
7567LqyhuqhvvLqyhvw101-Jan-2023 12:00:0031-May-2023 18:00:0067833600009
8568JhqhudoPwlphHqw220-Feb-2023 18:00:0031-May-2023 18:00:00667447000010
9569VhqrNlvhq101-Jan-2023 12:00:0031-May-2023 18:00:00665662000011
RANKSHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSCDBXRN
39599LqgrfklqdVklsslqj101-Jan-2023 12:00:0031-May-2023 18:00:00575101000041
40600FkledVklsslqj101-Jan-2023 12:00:0031-May-2023 18:00:00574448000042
41601KNJroghqDxwxpq101-Jan-2023 12:00:0031-May-2023 18:00:00573875000043
42602IxmldqCkrqjkdqj101-Jan-2023 12:00:0031-May-2023 18:00:00573635000044
43603MdbdVdpxgud201-Jan-2023 12:00:0031-May-2023 18:00:00571005000045
44604LVOICH202-Jan-2023 00:00:0029-May-2023 18:00:00570573000046
45605QlqjerKdlckrxVksj101-Jan-2023 12:00:0031-May-2023 18:00:00569677000047
46606LlqrNdlxqNdlvkd101-Jan-2023 12:00:0031-May-2023 12:00:00568379000048
47607IJDVShwuroFrOwg225-Apr-2023 00:00:0024-Apr-2023 00:00:00567904000049
48608CkrqjAlqPdulqhFr101-Jan-2023 12:00:0031-May-2023 18:00:00566234000050