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_001074

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_KIND has unique valuesUnique
RN has unique valuesUnique
FRGHT_CNVNC_QTY has 4 (8.2%) zerosZeros

Reproduction

Analysis started2023-12-10 14:58:03.679145
Analysis finished2023-12-10 14:58:07.543660
Duration3.86 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%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:58:07.709767image/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
2023-12-10T23:58:08.384474image/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%

SHIP_KIND
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:58:08.815583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length20
Mean length14.612245
Min length5

Characters and Unicode

Total characters716
Distinct characters50
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

Unique49 ?
Unique (%)100.0%

Sample

1st rowBULK CARRIER
2nd rowOre Carrier
3rd rowChip Carrier
4th rowChemical/Oil Product
5th rowGENERAL CARGO
ValueCountFrequency (%)
carrier 20
 
18.5%
cargo 6
 
5.6%
bulk 3
 
2.8%
general 3
 
2.8%
vessel 3
 
2.8%
or 3
 
2.8%
ship 3
 
2.8%
ore 3
 
2.8%
chips 2
 
1.9%
wood 2
 
1.9%
Other values (51) 60
55.6%
2023-12-10T23:58:09.516295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 77
 
10.8%
59
 
8.2%
e 58
 
8.1%
i 43
 
6.0%
a 42
 
5.9%
C 38
 
5.3%
R 29
 
4.1%
o 25
 
3.5%
l 25
 
3.5%
n 24
 
3.4%
Other values (40) 296
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 431
60.2%
Uppercase Letter 218
30.4%
Space Separator 59
 
8.2%
Other Punctuation 4
 
0.6%
Dash Punctuation 2
 
0.3%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 77
17.9%
e 58
13.5%
i 43
10.0%
a 42
9.7%
o 25
 
5.8%
l 25
 
5.8%
n 24
 
5.6%
s 20
 
4.6%
g 19
 
4.4%
t 14
 
3.2%
Other values (13) 84
19.5%
Uppercase Letter
ValueCountFrequency (%)
C 38
17.4%
R 29
13.3%
E 19
 
8.7%
O 16
 
7.3%
I 14
 
6.4%
S 13
 
6.0%
A 13
 
6.0%
L 11
 
5.0%
G 9
 
4.1%
B 8
 
3.7%
Other values (11) 48
22.0%
Other Punctuation
ValueCountFrequency (%)
/ 3
75.0%
& 1
 
25.0%
Space Separator
ValueCountFrequency (%)
59
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 649
90.6%
Common 67
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 77
 
11.9%
e 58
 
8.9%
i 43
 
6.6%
a 42
 
6.5%
C 38
 
5.9%
R 29
 
4.5%
o 25
 
3.9%
l 25
 
3.9%
n 24
 
3.7%
s 20
 
3.1%
Other values (34) 268
41.3%
Common
ValueCountFrequency (%)
59
88.1%
/ 3
 
4.5%
- 2
 
3.0%
& 1
 
1.5%
( 1
 
1.5%
) 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 77
 
10.8%
59
 
8.2%
e 58
 
8.1%
i 43
 
6.0%
a 42
 
5.9%
C 38
 
5.3%
R 29
 
4.1%
o 25
 
3.5%
l 25
 
3.5%
n 24
 
3.4%
Other values (40) 296
41.3%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.673469
Minimum1
Maximum2283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:58:09.793930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q319
95-th percentile257.4
Maximum2283
Range2282
Interquartile range (IQR)18

Descriptive statistics

Standard deviation334.08883
Coefficient of variation (CV)4.090543
Kurtosis41.372876
Mean81.673469
Median Absolute Deviation (MAD)2
Skewness6.2560974
Sum4002
Variance111615.35
MonotonicityNot monotonic
2023-12-10T23:58:10.007869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 18
36.7%
2 6
 
12.2%
9 3
 
6.1%
4 3
 
6.1%
3 2
 
4.1%
19 2
 
4.1%
2283 1
 
2.0%
5 1
 
2.0%
10 1
 
2.0%
39 1
 
2.0%
Other values (11) 11
22.4%
ValueCountFrequency (%)
1 18
36.7%
2 6
 
12.2%
3 2
 
4.1%
4 3
 
6.1%
5 1
 
2.0%
8 1
 
2.0%
9 3
 
6.1%
10 1
 
2.0%
11 1
 
2.0%
19 2
 
4.1%
ValueCountFrequency (%)
2283 1
2.0%
508 1
2.0%
263 1
2.0%
249 1
2.0%
237 1
2.0%
137 1
2.0%
48 1
2.0%
40 1
2.0%
39 1
2.0%
29 1
2.0%

DPTR_HMS
Date

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:02
Maximum2022-04-30 13:13:22
2023-12-10T23:58:10.260110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:10.542314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

ARVL_HMS
Date

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-05-23 07:51:58
Maximum2022-07-17 22:00:21
2023-12-10T23:58:10.797862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:11.053050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2706918 × 1010
Minimum0
Maximum2.27401 × 1012
Zeros4
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:58:11.306412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140955800
median3.27272 × 108
Q34.45918 × 109
95-th percentile1.496126 × 1011
Maximum2.27401 × 1012
Range2.27401 × 1012
Interquartile range (IQR)4.4182242 × 109

Descriptive statistics

Standard deviation3.7273844 × 1011
Coefficient of variation (CV)4.5067383
Kurtosis28.030696
Mean8.2706918 × 1010
Median Absolute Deviation (MAD)3.27272 × 108
Skewness5.2308974
Sum4.052639 × 1012
Variance1.3893394 × 1023
MonotonicityDecreasing
2023-12-10T23:58:11.549170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 4
 
8.2%
2274010000000.0 1
 
2.0%
54789100.0 1
 
2.0%
236925000.0 1
 
2.0%
231320000.0 1
 
2.0%
189776000.0 1
 
2.0%
91049500.0 1
 
2.0%
87125300.0 1
 
2.0%
79506100.0 1
 
2.0%
79313300.0 1
 
2.0%
Other values (36) 36
73.5%
ValueCountFrequency (%)
0.0 4
8.2%
3.38164e-36 1
 
2.0%
8.5727e-36 1
 
2.0%
2311660.0 1
 
2.0%
4718810.0 1
 
2.0%
10377200.0 1
 
2.0%
12180800.0 1
 
2.0%
16634500.0 1
 
2.0%
21573200.0 1
 
2.0%
40955800.0 1
 
2.0%
ValueCountFrequency (%)
2274010000000.0 1
2.0%
1336720000000.0 1
2.0%
165821000000.0 1
2.0%
125300000000.0 1
2.0%
40649800000.0 1
2.0%
17179600000.0 1
2.0%
17144700000.0 1
2.0%
16625200000.0 1
2.0%
9901350000.0 1
2.0%
9599450000.0 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
2023-12-10T23:58:11.848287image/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
2023-12-10T23:58:12.156161image/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

2023-12-10T23:58:06.460295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:04.456445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.101935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.804429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.658265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:04.638016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.287430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.970650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.823214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:04.789473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.442390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.128555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.987375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:04.939023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.595566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.287113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:58:12.347988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
RANK1.0001.0000.3690.8691.0000.1111.000
SHIP_KIND1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.3691.0001.0000.0000.0000.7490.369
DPTR_HMS0.8691.0000.0001.0000.9880.0000.869
ARVL_HMS1.0001.0000.0000.9881.0001.0001.000
FRGHT_CNVNC_QTY0.1111.0000.7490.0001.0001.0000.111
RN1.0001.0000.3690.8691.0000.1111.000
2023-12-10T23:58:12.590518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRN
RANK1.000-0.779-1.0001.000
SHIP_CNT-0.7791.0000.781-0.779
FRGHT_CNVNC_QTY-1.0000.7811.000-1.000
RN1.000-0.779-1.0001.000

Missing values

2023-12-10T23:58:07.211916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:58:07.439198image/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_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
02BULK CARRIER228301-Jan-2022 00:00:0217-Jul-2022 22:00:212274010000000.02
13Ore Carrier26301-Jan-2022 00:00:1017-Jul-2022 22:00:181336720000000.03
24Chip Carrier13701-Jan-2022 00:00:1917-Jul-2022 22:00:07165821000000.04
35Chemical/Oil Product50801-Jan-2022 00:00:1117-Jul-2022 22:00:19125300000000.05
46GENERAL CARGO4801-Jan-2022 00:00:2617-Jul-2022 21:59:4740649800000.06
57Bulk & Caustic Soda Carrier1101-Jan-2022 00:07:1017-Jul-2022 21:58:5317179600000.07
68Cargo24901-Jan-2022 00:01:3417-Jul-2022 21:59:5117144700000.08
79Cement Carrier23701-Jan-2022 00:00:0417-Jul-2022 22:00:1216625200000.09
810Slurry Carrier201-Jan-2022 02:29:5417-Jul-2022 21:50:309901350000.010
911SELF DISCHARGING BUL1901-Jan-2022 00:00:2117-Jul-2022 21:59:519599450000.011
RANKSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
3941Offshore Supply Ship101-Jan-2022 00:31:5717-Jul-2022 21:44:2712180800.041
4042Other501-Jan-2022 00:13:2205-Jun-2022 23:49:5910377200.042
4143Passenger101-Jan-2022 04:01:1605-Jun-2022 21:26:234718810.043
4244Tug or Supply Vessel101-Jan-2022 00:33:3505-Jun-2022 23:44:552311660.044
4345Anti-Pollution101-Jan-2022 00:50:1017-Jul-2022 21:58:330.045
4446Passenger Ship101-Jan-2022 03:09:4917-Jul-2022 21:43:580.046
4547Cardinal Mark N105-Jan-2022 16:52:4026-May-2022 04:40:200.047
4648Landing Craft119-Feb-2022 13:49:0708-Jul-2022 15:59:050.048
4749Light Vessel130-Apr-2022 13:13:2203-Jun-2022 22:02:110.049
4850FLOATING STORAGE/PRO201-Jan-2022 00:37:2705-Jun-2022 23:57:100.050