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_001302

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_TONM 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
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:36:30.451879
Analysis finished2023-12-10 14:36:33.558034
Duration3.11 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
2023-12-10T23:36:33.622442image/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
2023-12-10T23:36:33.751046image/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
2023-12-10T23:36:33.940583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.3877551
Min length4

Characters and Unicode

Total characters411
Distinct characters46
Distinct categories4 ?
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 rowUnited Kingdom
2nd rowGermany
3rd rowSpain
4th rowChina
5th rowIreland
ValueCountFrequency (%)
united 3
 
4.8%
new 2
 
3.2%
uruguay 1
 
1.6%
panama 1
 
1.6%
trinidad 1
 
1.6%
1
 
1.6%
tobago 1
 
1.6%
philippines 1
 
1.6%
algeria 1
 
1.6%
maldives 1
 
1.6%
Other values (49) 49
79.0%
2023-12-10T23:36:34.250372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 66
16.1%
i 37
 
9.0%
n 33
 
8.0%
e 28
 
6.8%
r 20
 
4.9%
l 19
 
4.6%
o 18
 
4.4%
t 16
 
3.9%
d 16
 
3.9%
u 14
 
3.4%
Other values (36) 144
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 336
81.8%
Uppercase Letter 61
 
14.8%
Space Separator 13
 
3.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 66
19.6%
i 37
11.0%
n 33
9.8%
e 28
8.3%
r 20
 
6.0%
l 19
 
5.7%
o 18
 
5.4%
t 16
 
4.8%
d 16
 
4.8%
u 14
 
4.2%
Other values (13) 69
20.5%
Uppercase Letter
ValueCountFrequency (%)
S 8
13.1%
A 6
 
9.8%
M 5
 
8.2%
U 4
 
6.6%
P 4
 
6.6%
C 4
 
6.6%
B 3
 
4.9%
I 3
 
4.9%
T 3
 
4.9%
G 3
 
4.9%
Other values (11) 18
29.5%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 397
96.6%
Common 14
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 66
16.6%
i 37
 
9.3%
n 33
 
8.3%
e 28
 
7.1%
r 20
 
5.0%
l 19
 
4.8%
o 18
 
4.5%
t 16
 
4.0%
d 16
 
4.0%
u 14
 
3.5%
Other values (34) 130
32.7%
Common
ValueCountFrequency (%)
13
92.9%
& 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 66
16.1%
i 37
 
9.0%
n 33
 
8.0%
e 28
 
6.8%
r 20
 
4.9%
l 19
 
4.6%
o 18
 
4.4%
t 16
 
3.9%
d 16
 
3.9%
u 14
 
3.4%
Other values (36) 144
35.0%

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

2023-12-10T23:36:34.360407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:36:34.430998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
etc 49
100.0%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.938776
Minimum5
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:36:34.508890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median17
Q325
95-th percentile64
Maximum148
Range143
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.243047
Coefficient of variation (CV)1.0568588
Kurtosis14.957028
Mean22.938776
Median Absolute Deviation (MAD)8
Skewness3.4708482
Sum1124
Variance587.72534
MonotonicityNot monotonic
2023-12-10T23:36:34.617012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
18 4
 
8.2%
8 4
 
8.2%
9 3
 
6.1%
11 3
 
6.1%
6 3
 
6.1%
15 3
 
6.1%
28 3
 
6.1%
19 2
 
4.1%
21 2
 
4.1%
17 2
 
4.1%
Other values (18) 20
40.8%
ValueCountFrequency (%)
5 2
4.1%
6 3
6.1%
7 1
 
2.0%
8 4
8.2%
9 3
6.1%
10 1
 
2.0%
11 3
6.1%
13 1
 
2.0%
15 3
6.1%
16 2
4.1%
ValueCountFrequency (%)
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%
26 1
 
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 03:27:16
Maximum2023-02-27 11:38:32
2023-12-10T23:36:34.727662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:34.837713image/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
Minimum2023-04-06 03:48:42
Maximum2023-05-31 23:59:22
2023-12-10T23:36:34.944255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:35.052174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

FRGHT_CNVNC_QTY_TONM
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145056.33
Minimum38950
Maximum764240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:36:35.162181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38950
5-th percentile41295.2
Q169208
median104550
Q3164246
95-th percentile410000
Maximum764240
Range725290
Interquartile range (IQR)95038

Descriptive statistics

Standard deviation135037.54
Coefficient of variation (CV)0.93093178
Kurtosis9.6307289
Mean145056.33
Median Absolute Deviation (MAD)45920
Skewness2.8430616
Sum7107760
Variance1.8235138 × 1010
MonotonicityDecreasing
2023-12-10T23:36:35.273556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
104550 2
 
4.1%
103484 1
 
2.0%
100286 1
 
2.0%
90938 1
 
2.0%
85608 1
 
2.0%
85362 1
 
2.0%
84542 1
 
2.0%
78310 1
 
2.0%
76260 1
 
2.0%
71586 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
38950 1
2.0%
39442 1
2.0%
40180 1
2.0%
42968 1
2.0%
43296 1
2.0%
46330 1
2.0%
49774 1
2.0%
51660 1
2.0%
55268 1
2.0%
60680 1
2.0%
ValueCountFrequency (%)
764240 1
2.0%
519962 1
2.0%
467892 1
2.0%
323162 1
2.0%
295200 1
2.0%
261990 1
2.0%
251576 1
2.0%
207706 1
2.0%
180318 1
2.0%
169576 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
2023-12-10T23:36:35.390809image/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
2023-12-10T23:36:35.527459image/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

2023-12-10T23:36:32.816188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:31.879762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.202206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.509633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.893087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:31.985118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.272324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.582963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.980269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.059829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.357088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.660220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:33.057583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.134872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.437808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:36:32.742725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:36:35.607271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
RANK1.0001.0000.5821.0001.0000.7231.000
DPTR_CN_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.5821.0001.0001.0001.0000.9900.582
DPTR_HMS1.0001.0001.0001.0001.0001.0001.000
ARVL_HMS1.0001.0001.0001.0001.0001.0001.000
FRGHT_CNVNC_QTY_TONM0.7231.0000.9901.0001.0001.0000.723
RN1.0001.0000.5821.0001.0000.7231.000
2023-12-10T23:36:35.703282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTY_TONMRN
RANK1.000-0.968-1.0001.000
SHIP_CNT-0.9681.0000.968-0.968
FRGHT_CNVNC_QTY_TONM-1.0000.9681.000-1.000
RN1.000-0.968-1.0001.000

Missing values

2023-12-10T23:36:33.146034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:36:33.512800image/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_QTY_TONMRN
02United Kingdometc14801-Jan-2023 03:27:1631-May-2023 22:54:577642402
13Germanyetc8303-Jan-2023 00:05:3831-May-2023 23:58:445199623
24Spainetc7201-Jan-2023 12:18:1431-May-2023 23:55:144678924
35Chinaetc4701-Jan-2023 15:04:1227-May-2023 00:52:373231625
46Irelandetc5201-Jan-2023 21:17:4831-May-2023 23:58:382952006
57Portugaletc4307-Jan-2023 08:34:3031-May-2023 23:58:492619907
68Singaporeetc3502-Jan-2023 06:10:5131-May-2023 07:22:482515768
79United Statesetc2804-Jan-2023 15:45:2431-May-2023 23:48:272077069
810Belgiumetc2802-Jan-2023 07:09:3025-May-2023 15:16:4618031810
911Jamaicaetc2602-Jan-2023 01:01:4129-May-2023 01:43:4116957611
RANKDPTR_CN_NMSHIP_KINDSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
3941Guatemalaetc1008-Jan-2023 18:44:5110-Apr-2023 04:03:466068041
4042United Arab Emiratesetc807-Feb-2023 03:29:4815-May-2023 03:19:195526842
4143Indiaetc804-Jan-2023 12:03:3513-May-2023 06:16:125166043
4244Mexicoetc608-Feb-2023 18:32:1725-May-2023 00:52:014977444
4345The Bahamasetc516-Jan-2023 11:38:4031-May-2023 23:54:524633045
4446New Zealandetc718-Jan-2023 09:29:1219-May-2023 17:09:444329646
4547Angolaetc619-Jan-2023 15:50:5231-May-2023 13:09:184296847
4648Surinameetc803-Jan-2023 04:37:4729-May-2023 01:48:054018048
4749Japanetc627-Feb-2023 11:38:3231-May-2023 04:15:063944249
4850Argentinaetc513-Jan-2023 00:25:2929-May-2023 00:08:003895050