Overview

Dataset statistics

Number of variables8
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory70.7 B

Variable types

Numeric4
Text1
Categorical1
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001069

Alerts

RANK is highly overall correlated with SHIP_CNT and 2 other fieldsHigh correlation
SHIP_CNT is highly overall correlated with RANK and 3 other fieldsHigh correlation
FRGHT_CNVNC_QTY_TONM is highly overall correlated with RANK and 2 other fieldsHigh correlation
RN is highly overall correlated with RANK and 2 other fieldsHigh correlation
SHIP_KIND is highly overall correlated with SHIP_CNTHigh correlation
RANK has unique valuesUnique
DPTR_HMS has unique valuesUnique
FRGHT_CNVNC_QTY_TONM has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:27:09.592331
Analysis finished2023-12-10 14:27:11.028250
Duration1.44 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
2023-12-10T23:27:11.085260image/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
2023-12-10T23:27:11.191691image/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

Distinct36
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:27:11.356249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length16
Mean length8.7959184
Min length5

Characters and Unicode

Total characters431
Distinct characters44
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 rowChina
2nd rowIndonesia
3rd rowSouth Africa
4th rowIndia
5th rowSpain
ValueCountFrequency (%)
united 6
 
9.4%
kingdom 3
 
4.7%
denmark 2
 
3.1%
states 2
 
3.1%
netherlands 2
 
3.1%
brazil 2
 
3.1%
morocco 2
 
3.1%
spain 2
 
3.1%
egypt 2
 
3.1%
canada 2
 
3.1%
Other values (36) 39
60.9%
2023-12-10T23:27:11.826596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 55
 
12.8%
i 40
 
9.3%
n 39
 
9.0%
e 32
 
7.4%
r 30
 
7.0%
t 26
 
6.0%
d 17
 
3.9%
15
 
3.5%
o 14
 
3.2%
u 14
 
3.2%
Other values (34) 149
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 352
81.7%
Uppercase Letter 64
 
14.8%
Space Separator 15
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 55
15.6%
i 40
11.4%
n 39
11.1%
e 32
9.1%
r 30
8.5%
t 26
 
7.4%
d 17
 
4.8%
o 14
 
4.0%
u 14
 
4.0%
s 12
 
3.4%
Other values (12) 73
20.7%
Uppercase Letter
ValueCountFrequency (%)
S 8
12.5%
U 8
12.5%
A 6
 
9.4%
I 5
 
7.8%
M 5
 
7.8%
T 4
 
6.2%
E 3
 
4.7%
C 3
 
4.7%
N 3
 
4.7%
K 3
 
4.7%
Other values (11) 16
25.0%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 416
96.5%
Common 15
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 55
13.2%
i 40
 
9.6%
n 39
 
9.4%
e 32
 
7.7%
r 30
 
7.2%
t 26
 
6.2%
d 17
 
4.1%
o 14
 
3.4%
u 14
 
3.4%
s 12
 
2.9%
Other values (33) 137
32.9%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 55
 
12.8%
i 40
 
9.3%
n 39
 
9.0%
e 32
 
7.4%
r 30
 
7.0%
t 26
 
6.0%
d 17
 
3.9%
15
 
3.5%
o 14
 
3.2%
u 14
 
3.2%
Other values (34) 149
34.6%

SHIP_KIND
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Bulk Carrier
35 
BULK CARRIER
12 
Chemical/Oil Product
 
2

Length

Max length20
Median length12
Mean length12.326531
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBulk Carrier
2nd rowBulk Carrier
3rd rowBulk Carrier
4th rowBulk Carrier
5th rowBulk Carrier

Common Values

ValueCountFrequency (%)
Bulk Carrier 35
71.4%
BULK CARRIER 12
 
24.5%
Chemical/Oil Product 2
 
4.1%

Length

2023-12-10T23:27:11.942098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:27:12.028393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
bulk 47
48.0%
carrier 47
48.0%
chemical/oil 2
 
2.0%
product 2
 
2.0%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.142857
Minimum2
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:27:12.112327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median8
Q315
95-th percentile32
Maximum49
Range47
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.667318
Coefficient of variation (CV)0.87848499
Kurtosis4.091641
Mean12.142857
Median Absolute Deviation (MAD)4
Skewness1.9671575
Sum595
Variance113.79167
MonotonicityNot monotonic
2023-12-10T23:27:12.258783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5 7
14.3%
8 5
 
10.2%
3 5
 
10.2%
9 4
 
8.2%
6 4
 
8.2%
11 3
 
6.1%
15 2
 
4.1%
4 2
 
4.1%
17 2
 
4.1%
29 1
 
2.0%
Other values (14) 14
28.6%
ValueCountFrequency (%)
2 1
 
2.0%
3 5
10.2%
4 2
 
4.1%
5 7
14.3%
6 4
8.2%
7 1
 
2.0%
8 5
10.2%
9 4
8.2%
11 3
6.1%
12 1
 
2.0%
ValueCountFrequency (%)
49 1
2.0%
48 1
2.0%
34 1
2.0%
29 1
2.0%
27 1
2.0%
26 1
2.0%
23 1
2.0%
21 1
2.0%
19 1
2.0%
17 2
4.1%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:37
Maximum2022-05-24 15:43:55
2023-12-10T23:27:12.379946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:12.506503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Date

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-02-09 17:35:11
Maximum2022-07-17 21:59:22
2023-12-10T23:27:12.625712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:12.746891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

FRGHT_CNVNC_QTY_TONM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean639474.47
Minimum156901
Maximum2706130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:27:12.872499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum156901
5-th percentile170016.2
Q1240221
median398438
Q3743089
95-th percentile2013688
Maximum2706130
Range2549229
Interquartile range (IQR)502868

Descriptive statistics

Standard deviation604027.59
Coefficient of variation (CV)0.94456873
Kurtosis3.3198078
Mean639474.47
Median Absolute Deviation (MAD)197781
Skewness1.9098035
Sum31334249
Variance3.6484933 × 1011
MonotonicityStrictly decreasing
2023-12-10T23:27:13.001651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2706130 1
 
2.0%
233474 1
 
2.0%
325702 1
 
2.0%
317849 1
 
2.0%
309139 1
 
2.0%
300153 1
 
2.0%
292221 1
 
2.0%
275013 1
 
2.0%
267789 1
 
2.0%
262892 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
156901 1
2.0%
166874 1
2.0%
167891 1
2.0%
173204 1
2.0%
178410 1
2.0%
188219 1
2.0%
192313 1
2.0%
200657 1
2.0%
203911 1
2.0%
213697 1
2.0%
ValueCountFrequency (%)
2706130 1
2.0%
2385540 1
2.0%
2155180 1
2.0%
1801450 1
2.0%
1545630 1
2.0%
1521600 1
2.0%
1104700 1
2.0%
1070400 1
2.0%
1056690 1
2.0%
1053900 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
2023-12-10T23:27:13.127236image/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
2023-12-10T23:27:13.250349image/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

2023-12-10T23:27:10.594407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:09.869015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.106980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.353936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.654832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:09.928441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.167885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.417079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.716130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:09.991093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.227111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.475474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.782809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.050586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.292322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:27:10.532762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:27:13.336022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKARVL_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
RANK1.0000.3880.5920.6261.0000.9170.7871.000
ARVL_CN_NM0.3881.0000.0000.0481.0000.9860.9660.388
SHIP_KIND0.5920.0001.0000.7651.0001.0000.0000.592
SHIP_CNT0.6260.0480.7651.0001.0000.7270.7710.626
DPTR_HMS1.0001.0001.0001.0001.0001.0001.0001.000
ARVL_HMS0.9170.9861.0000.7271.0001.0000.0000.917
FRGHT_CNVNC_QTY_TONM0.7870.9660.0000.7711.0000.0001.0000.787
RN1.0000.3880.5920.6261.0000.9170.7871.000
2023-12-10T23:27:13.438641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTY_TONMRNSHIP_KIND
RANK1.000-0.702-1.0001.0000.379
SHIP_CNT-0.7021.0000.702-0.7020.634
FRGHT_CNVNC_QTY_TONM-1.0000.7021.000-1.0000.000
RN1.000-0.702-1.0001.0000.379
SHIP_KIND0.3790.6340.0000.3791.000

Missing values

2023-12-10T23:27:10.889136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:27:10.991173image/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_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
02ChinaBulk Carrier2901-Jan-2022 00:13:4415-Jul-2022 10:47:0027061302
13IndonesiaBulk Carrier3401-Jan-2022 00:00:3704-Jul-2022 12:54:5323855403
24South AfricaBulk Carrier2601-Jan-2022 00:19:3417-Jul-2022 21:59:2221551804
35IndiaBulk Carrier1605-Jan-2022 10:22:3706-Jun-2022 01:48:5318014505
46SpainBulk Carrier2301-Jan-2022 00:04:2922-Jun-2022 17:57:3415456306
57SingaporeBulk Carrier1412-Jan-2022 01:06:4903-Jul-2022 20:46:1715216007
68BrazilBulk Carrier2101-Jan-2022 18:25:0717-Jul-2022 21:53:3411047008
79AustraliaBulk Carrier2701-Jan-2022 01:43:5017-Jul-2022 21:49:1510704009
810Papua New GuineaBulk Carrier1108-Jan-2022 11:52:0607-Jul-2022 22:57:49105669010
911EgyptBulk Carrier1301-Jan-2022 03:38:2610-May-2022 07:53:14105390011
RANKARVL_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
3941United Arab EmiratesBulk Carrier505-Mar-2022 06:27:3007-Apr-2022 19:03:3421369741
4042DenmarkBULK CARRIER613-Jan-2022 03:30:5905-Jun-2022 11:43:0120391142
4143CanadaBulk Carrier619-Jan-2022 06:52:5919-Jun-2022 14:53:2520065743
4244British Indian Ocean TerritoryBulk Carrier224-May-2022 15:43:5505-Jun-2022 23:19:0119231344
4345EgyptBULK CARRIER501-Jan-2022 04:46:2705-Mar-2022 12:26:5318821945
4446GibraltarBulk Carrier404-Mar-2022 02:17:2023-Jun-2022 03:55:1617841046
4547TurkeyBULK CARRIER521-Jan-2022 05:04:4714-May-2022 20:38:2217320447
4648MalaysiaBULK CARRIER310-Jan-2022 03:36:0719-Apr-2022 02:56:4216789148
4749VietnamBulk Carrier315-Apr-2022 00:10:1501-Jul-2022 22:42:3416687449
4850United KingdomBULK CARRIER517-Jan-2022 07:16:5203-Jun-2022 04:56:2515690150