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_001301

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_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
ARVL_CN_NM has unique valuesUnique
FRGHT_CNVNC_QTY_TONM has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:32:05.822778
Analysis finished2023-12-10 14:32:09.419877
Duration3.6 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
2023-12-10T23:32:09.503408image/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
2023-12-10T23:32:09.657301image/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

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:32:09.877569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length7.5510204
Min length4

Characters and Unicode

Total characters370
Distinct characters45
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

Unique49 ?
Unique (%)100.0%

Sample

1st rowSingapore
2nd rowSouth Korea
3rd rowMalaysia
4th rowUnited States
5th rowSpain
ValueCountFrequency (%)
united 3
 
5.3%
south 2
 
3.5%
oman 1
 
1.8%
thailand 1
 
1.8%
sri 1
 
1.8%
lanka 1
 
1.8%
sweden 1
 
1.8%
brazil 1
 
1.8%
colombia 1
 
1.8%
philippines 1
 
1.8%
Other values (44) 44
77.2%
2023-12-10T23:32:10.265196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 56
15.1%
i 31
 
8.4%
n 27
 
7.3%
e 26
 
7.0%
r 25
 
6.8%
o 18
 
4.9%
t 18
 
4.9%
l 15
 
4.1%
d 14
 
3.8%
s 11
 
3.0%
Other values (35) 129
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 305
82.4%
Uppercase Letter 57
 
15.4%
Space Separator 8
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 56
18.4%
i 31
10.2%
n 27
8.9%
e 26
8.5%
r 25
 
8.2%
o 18
 
5.9%
t 18
 
5.9%
l 15
 
4.9%
d 14
 
4.6%
s 11
 
3.6%
Other values (13) 64
21.0%
Uppercase Letter
ValueCountFrequency (%)
S 8
14.0%
P 6
 
10.5%
T 4
 
7.0%
A 4
 
7.0%
M 4
 
7.0%
I 4
 
7.0%
G 3
 
5.3%
E 3
 
5.3%
N 3
 
5.3%
U 3
 
5.3%
Other values (11) 15
26.3%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 362
97.8%
Common 8
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 56
15.5%
i 31
 
8.6%
n 27
 
7.5%
e 26
 
7.2%
r 25
 
6.9%
o 18
 
5.0%
t 18
 
5.0%
l 15
 
4.1%
d 14
 
3.9%
s 11
 
3.0%
Other values (34) 121
33.4%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 56
15.1%
i 31
 
8.4%
n 27
 
7.3%
e 26
 
7.0%
r 25
 
6.8%
o 18
 
4.9%
t 18
 
4.9%
l 15
 
4.1%
d 14
 
3.8%
s 11
 
3.0%
Other values (35) 129
34.9%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2739.1224
Minimum605
Maximum11219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:10.409941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum605
5-th percentile714.2
Q11148
median1955
Q33507
95-th percentile6953.8
Maximum11219
Range10614
Interquartile range (IQR)2359

Descriptive statistics

Standard deviation2237.9185
Coefficient of variation (CV)0.81702026
Kurtosis3.3340163
Mean2739.1224
Median Absolute Deviation (MAD)1152
Skewness1.6962249
Sum134217
Variance5008279.4
MonotonicityNot monotonic
2023-12-10T23:32:10.543308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
771 2
 
4.1%
11219 1
 
2.0%
1120 1
 
2.0%
1824 1
 
2.0%
1405 1
 
2.0%
1244 1
 
2.0%
1393 1
 
2.0%
1285 1
 
2.0%
1249 1
 
2.0%
1213 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
605 1
2.0%
648 1
2.0%
699 1
2.0%
737 1
2.0%
771 2
4.1%
778 1
2.0%
802 1
2.0%
949 1
2.0%
959 1
2.0%
1053 1
2.0%
ValueCountFrequency (%)
11219 1
2.0%
7606 1
2.0%
7259 1
2.0%
6496 1
2.0%
6435 1
2.0%
5925 1
2.0%
5623 1
2.0%
4459 1
2.0%
4191 1
2.0%
4187 1
2.0%

DPTR_HMS
Date

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 00:00:01
Maximum2023-01-01 00:00:16
2023-12-10T23:32:10.694723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:10.805620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

ARVL_HMS
Date

Distinct26
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-05-31 22:51:02
Maximum2023-05-31 23:59:59
2023-12-10T23:32:10.912055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:11.009202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

FRGHT_CNVNC_QTY_TONM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19190287
Minimum4866860
Maximum80762400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:11.155322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4866860
5-th percentile5052824
Q18236160
median13862300
Q324168100
95-th percentile49074580
Maximum80762400
Range75895540
Interquartile range (IQR)15931940

Descriptive statistics

Standard deviation15684254
Coefficient of variation (CV)0.81730165
Kurtosis3.8630056
Mean19190287
Median Absolute Deviation (MAD)8128200
Skewness1.7574273
Sum9.4032408 × 108
Variance2.4599581 × 1014
MonotonicityStrictly decreasing
2023-12-10T23:32:11.313844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
80762400 1
 
2.0%
7880530 1
 
2.0%
10450500 1
 
2.0%
9604910 1
 
2.0%
9580470 1
 
2.0%
9483550 1
 
2.0%
9269940 1
 
2.0%
9207780 1
 
2.0%
9049600 1
 
2.0%
8835010 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
4866860 1
2.0%
4894910 1
2.0%
4970920 1
2.0%
5175680 1
2.0%
5366160 1
2.0%
5632250 1
2.0%
5734100 1
2.0%
5803800 1
2.0%
6344830 1
2.0%
6440120 1
2.0%
ValueCountFrequency (%)
80762400 1
2.0%
50830400 1
2.0%
50327500 1
2.0%
47195200 1
2.0%
41644600 1
2.0%
41265600 1
2.0%
35653900 1
2.0%
35482600 1
2.0%
31182500 1
2.0%
28998600 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
2023-12-10T23:32:11.492327image/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
2023-12-10T23:32:11.687622image/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

2023-12-10T23:32:08.828220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:07.388791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.108715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.433935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.916297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:07.538484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.191763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.527100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.999171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:07.623314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.268775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.621131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:09.099117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:07.708557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.359570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:08.728640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:32:11.803975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKARVL_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
RANK1.0001.0000.8100.6310.3000.8291.000
ARVL_CN_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.8101.0001.0000.0000.7800.9820.810
DPTR_HMS0.6311.0000.0001.0000.9530.0000.631
ARVL_HMS0.3001.0000.7800.9531.0000.3920.300
FRGHT_CNVNC_QTY_TONM0.8291.0000.9820.0000.3921.0000.829
RN1.0001.0000.8100.6310.3000.8291.000
2023-12-10T23:32:11.960175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTY_TONMRN
RANK1.000-0.985-1.0001.000
SHIP_CNT-0.9851.0000.985-0.985
FRGHT_CNVNC_QTY_TONM-1.0000.9851.000-1.000
RN1.000-0.985-1.0001.000

Missing values

2023-12-10T23:32:09.222959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:32:09.363839image/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_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
02Singapore1121901-Jan-2023 00:00:0131-May-2023 23:59:02807624002
13South Korea760601-Jan-2023 00:00:0131-May-2023 23:59:58508304003
24Malaysia725901-Jan-2023 00:00:0231-May-2023 23:59:56503275004
35United States649601-Jan-2023 00:00:0131-May-2023 23:59:59471952005
46Spain592501-Jan-2023 00:00:0131-May-2023 23:59:58416446006
57Netherlands643501-Jan-2023 00:00:0131-May-2023 23:59:53412656007
68Japan562301-Jan-2023 00:00:0131-May-2023 23:59:57356539008
79Egypt402301-Jan-2023 00:00:0531-May-2023 23:59:48354826009
810United Arab Emirates419101-Jan-2023 00:00:0331-May-2023 23:59:423118250010
911United Kingdom445901-Jan-2023 00:00:0131-May-2023 23:59:502899860011
RANKARVL_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
3941South Africa77801-Jan-2023 00:00:0131-May-2023 23:59:58644012041
4042Poland94901-Jan-2023 00:00:1631-May-2023 23:59:07634483042
4143Peru77101-Jan-2023 00:00:0131-May-2023 23:59:54580380043
4244Nigeria80201-Jan-2023 00:00:0131-May-2023 23:59:43573410044
4345Finland95901-Jan-2023 00:00:0131-May-2023 23:58:32563225045
4446Togo77101-Jan-2023 00:00:0631-May-2023 22:51:02536616046
4547Israel73701-Jan-2023 00:00:0631-May-2023 23:58:49517568047
4648Pakistan60501-Jan-2023 00:00:1231-May-2023 23:59:53497092048
4749Ecuador69901-Jan-2023 00:00:0131-May-2023 23:59:58489491049
4850Qatar64801-Jan-2023 00:00:0131-May-2023 23:58:40486686050