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_001311

Alerts

SHIP_KIND has constant value ""Constant
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
DPTR_CN_NM 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 14:25:17.570662
Analysis finished2023-12-10 14:25:19.847101
Duration2.28 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
2023-12-10T23:25:19.941786image/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
2023-12-10T23:25:20.124563image/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%

DPTR_CN_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:25:20.418862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length16
Mean length8.6326531
Min length4

Characters and Unicode

Total characters423
Distinct characters48
Distinct categories6 ?
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 rowNetherlands
2nd rowChina
3rd rowSingapore
4th rowSpain
5th rowGermany
ValueCountFrequency (%)
new 3
 
4.6%
united 2
 
3.1%
south 2
 
3.1%
guinea 1
 
1.5%
papua 1
 
1.5%
poland 1
 
1.5%
denmark 1
 
1.5%
india 1
 
1.5%
ghana 1
 
1.5%
malaysia 1
 
1.5%
Other values (51) 51
78.5%
2023-12-10T23:25:20.845920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 66
15.6%
n 35
 
8.3%
i 34
 
8.0%
e 29
 
6.9%
o 24
 
5.7%
t 19
 
4.5%
r 17
 
4.0%
l 17
 
4.0%
16
 
3.8%
u 13
 
3.1%
Other values (38) 153
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 340
80.4%
Uppercase Letter 64
 
15.1%
Space Separator 16
 
3.8%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 66
19.4%
n 35
10.3%
i 34
10.0%
e 29
 
8.5%
o 24
 
7.1%
t 19
 
5.6%
r 17
 
5.0%
l 17
 
5.0%
u 13
 
3.8%
d 13
 
3.8%
Other values (13) 73
21.5%
Uppercase Letter
ValueCountFrequency (%)
C 7
 
10.9%
S 7
 
10.9%
P 6
 
9.4%
G 5
 
7.8%
I 5
 
7.8%
N 4
 
6.2%
A 3
 
4.7%
F 3
 
4.7%
T 3
 
4.7%
L 3
 
4.7%
Other values (11) 18
28.1%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 404
95.5%
Common 19
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 66
16.3%
n 35
 
8.7%
i 34
 
8.4%
e 29
 
7.2%
o 24
 
5.9%
t 19
 
4.7%
r 17
 
4.2%
l 17
 
4.2%
u 13
 
3.2%
d 13
 
3.2%
Other values (34) 137
33.9%
Common
ValueCountFrequency (%)
16
84.2%
) 1
 
5.3%
( 1
 
5.3%
& 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 66
15.6%
n 35
 
8.3%
i 34
 
8.0%
e 29
 
6.9%
o 24
 
5.7%
t 19
 
4.5%
r 17
 
4.0%
l 17
 
4.0%
16
 
3.8%
u 13
 
3.1%
Other values (38) 153
36.2%

SHIP_KIND
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
etc
49 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
etc 49
100.0%

Length

2023-12-10T23:25:21.004662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:25:21.120951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
etc 49
100.0%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.591837
Minimum1
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:21.226465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q16
median16
Q326
95-th percentile78.6
Maximum181
Range180
Interquartile range (IQR)20

Descriptive statistics

Standard deviation33.978497
Coefficient of variation (CV)1.3816982
Kurtosis11.878302
Mean24.591837
Median Absolute Deviation (MAD)10
Skewness3.2633374
Sum1205
Variance1154.5383
MonotonicityNot monotonic
2023-12-10T23:25:21.408979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
6 3
 
6.1%
28 3
 
6.1%
3 3
 
6.1%
4 3
 
6.1%
18 3
 
6.1%
15 2
 
4.1%
1 2
 
4.1%
5 2
 
4.1%
11 2
 
4.1%
21 2
 
4.1%
Other values (22) 24
49.0%
ValueCountFrequency (%)
1 2
4.1%
2 1
 
2.0%
3 3
6.1%
4 3
6.1%
5 2
4.1%
6 3
6.1%
7 1
 
2.0%
8 1
 
2.0%
9 1
 
2.0%
10 1
 
2.0%
ValueCountFrequency (%)
181 1
 
2.0%
148 1
 
2.0%
83 1
 
2.0%
72 1
 
2.0%
52 1
 
2.0%
47 1
 
2.0%
43 1
 
2.0%
35 1
 
2.0%
31 1
 
2.0%
28 3
6.1%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 03:27:16
Maximum2023-03-30 00:39:08
2023-12-10T23:25:21.579875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.750950image/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
Minimum2023-03-22 02:32:28
Maximum2023-05-31 23:58:49
2023-12-10T23:25:21.918941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:22.044252image/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%
Mean1.345925 × 108
Minimum44712600
Maximum4.65715 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:25:22.206841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44712600
5-th percentile46840120
Q165145700
median95696300
Q31.54573 × 108
95-th percentile3.221212 × 108
Maximum4.65715 × 108
Range4.210024 × 108
Interquartile range (IQR)89427300

Descriptive statistics

Standard deviation99107306
Coefficient of variation (CV)0.73635088
Kurtosis2.2543889
Mean1.345925 × 108
Median Absolute Deviation (MAD)39040700
Skewness1.6129517
Sum6.5950325 × 109
Variance9.822258 × 1015
MonotonicityStrictly decreasing
2023-12-10T23:25:22.380298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
465715000 1
 
2.0%
63749300 1
 
2.0%
85496000 1
 
2.0%
83496700 1
 
2.0%
82748500 1
 
2.0%
81984300 1
 
2.0%
79432000 1
 
2.0%
78178700 1
 
2.0%
75205300 1
 
2.0%
68746900 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
44712600 1
2.0%
45496400 1
2.0%
46410200 1
2.0%
47485000 1
2.0%
50159800 1
2.0%
52738300 1
2.0%
54718600 1
2.0%
55895600 1
2.0%
57224800 1
2.0%
57921500 1
2.0%
ValueCountFrequency (%)
465715000 1
2.0%
409828000 1
2.0%
325420000 1
2.0%
317173000 1
2.0%
270460000 1
2.0%
268068000 1
2.0%
264382000 1
2.0%
255811000 1
2.0%
252931000 1
2.0%
205111000 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
2023-12-10T23:25:22.517458image/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
2023-12-10T23:25:22.683271image/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

2023-12-10T23:25:19.275779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:17.920097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.296465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.908816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.384856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.021161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.381275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.993106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.471576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.137050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.482107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.102177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.559458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.218524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.572750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.194541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:25:22.795626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
RANK1.0001.0000.0921.0001.0000.7731.000
DPTR_CN_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.0921.0001.0001.0001.0000.8230.092
DPTR_HMS1.0001.0001.0001.0001.0001.0001.000
ARVL_HMS1.0001.0001.0001.0001.0001.0001.000
FRGHT_CNVNC_QTY0.7731.0000.8231.0001.0001.0000.773
RN1.0001.0000.0921.0001.0000.7731.000
2023-12-10T23:25:22.895319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRN
RANK1.000-0.738-1.0001.000
SHIP_CNT-0.7381.0000.738-0.738
FRGHT_CNVNC_QTY-1.0000.7381.000-1.000
RN1.000-0.738-1.0001.000

Missing values

2023-12-10T23:25:19.673976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:25:19.797576image/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_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
02Netherlandsetc18102-Jan-2023 17:30:5231-May-2023 13:44:254657150002
13Chinaetc4701-Jan-2023 15:04:1227-May-2023 00:52:374098280003
24Singaporeetc3502-Jan-2023 06:10:5131-May-2023 07:22:483254200004
35Spainetc7201-Jan-2023 12:18:1431-May-2023 23:55:143171730005
46Germanyetc8303-Jan-2023 00:05:3831-May-2023 23:58:442704600006
57United Statesetc2804-Jan-2023 15:45:2431-May-2023 23:48:272680680007
68United Kingdometc14801-Jan-2023 03:27:1631-May-2023 22:54:572643820008
79Colombiaetc1103-Jan-2023 14:29:4221-May-2023 23:40:472558110009
810Dominican Republicetc2105-Jan-2023 18:58:4031-May-2023 23:53:5725293100010
911Portugaletc4307-Jan-2023 08:34:3031-May-2023 23:58:4920511100011
RANKDPTR_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
3941Lithuaniaetc2310-Jan-2023 03:57:1521-May-2023 02:20:555792150041
4042Trinidad & Tobagoetc1507-Jan-2023 11:40:1231-May-2023 20:30:295722480042
4143Cote dIvoire (Ivory Coast)etc315-Jan-2023 19:59:4227-May-2023 10:38:315589560043
4244Reunionetc130-Mar-2023 00:39:0811-Apr-2023 08:51:045471860044
4345Latviaetc1808-Jan-2023 14:02:0231-May-2023 23:54:145273830045
4446Italyetc904-Jan-2023 11:49:5331-May-2023 07:41:445015980046
4547Egyptetc112-Mar-2023 02:55:4822-Mar-2023 02:32:284748500047
4648Papua New Guineaetc316-Jan-2023 23:52:0328-Mar-2023 09:37:344641020048
4749Senegaletc408-Jan-2023 21:42:0515-May-2023 22:22:054549640049
4850Fijietc428-Jan-2023 19:40:2211-May-2023 09:59:374471260050