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_001066

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
DPTR_CN_NM has unique valuesUnique
SHIP_CNT has unique valuesUnique
FRGHT_CNVNC_QTY_TONM has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:44:10.039168
Analysis finished2023-12-10 14:44:12.275393
Duration2.24 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
2023-12-10T23:44:12.344084image/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
2023-12-10T23:44:12.470700image/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
2023-12-10T23:44:12.689553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.7755102
Min length4

Characters and Unicode

Total characters381
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 rowAustralia
2nd rowMalaysia
3rd rowChina
4th rowSpain
5th rowTaiwan
ValueCountFrequency (%)
united 3
 
5.0%
south 2
 
3.3%
australia 1
 
1.7%
arabia 1
 
1.7%
korea 1
 
1.7%
uruguay 1
 
1.7%
oman 1
 
1.7%
argentina 1
 
1.7%
greece 1
 
1.7%
sweden 1
 
1.7%
Other values (47) 47
78.3%
2023-12-10T23:44:13.098239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 59
15.5%
i 31
 
8.1%
e 29
 
7.6%
n 27
 
7.1%
r 26
 
6.8%
t 19
 
5.0%
u 15
 
3.9%
l 13
 
3.4%
o 12
 
3.1%
m 11
 
2.9%
Other values (35) 139
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 310
81.4%
Uppercase Letter 60
 
15.7%
Space Separator 11
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 59
19.0%
i 31
10.0%
e 29
9.4%
n 27
 
8.7%
r 26
 
8.4%
t 19
 
6.1%
u 15
 
4.8%
l 13
 
4.2%
o 12
 
3.9%
m 11
 
3.5%
Other values (12) 68
21.9%
Uppercase Letter
ValueCountFrequency (%)
S 8
13.3%
P 5
 
8.3%
A 5
 
8.3%
C 4
 
6.7%
U 4
 
6.7%
T 4
 
6.7%
M 4
 
6.7%
G 4
 
6.7%
N 3
 
5.0%
I 3
 
5.0%
Other values (12) 16
26.7%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 370
97.1%
Common 11
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 59
15.9%
i 31
 
8.4%
e 29
 
7.8%
n 27
 
7.3%
r 26
 
7.0%
t 19
 
5.1%
u 15
 
4.1%
l 13
 
3.5%
o 12
 
3.2%
m 11
 
3.0%
Other values (34) 128
34.6%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 59
15.5%
i 31
 
8.1%
e 29
 
7.6%
n 27
 
7.1%
r 26
 
6.8%
t 19
 
5.0%
u 15
 
3.9%
l 13
 
3.4%
o 12
 
3.1%
m 11
 
2.9%
Other values (35) 139
36.5%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2389.5306
Minimum315
Maximum7703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:13.300033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum315
5-th percentile437
Q1947
median2154
Q33015
95-th percentile5761.8
Maximum7703
Range7388
Interquartile range (IQR)2068

Descriptive statistics

Standard deviation1758.8067
Coefficient of variation (CV)0.73604696
Kurtosis1.1707822
Mean2389.5306
Median Absolute Deviation (MAD)1124
Skewness1.1744316
Sum117087
Variance3093401.2
MonotonicityNot monotonic
2023-12-10T23:44:13.468127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5485 1
 
2.0%
768 1
 
2.0%
2670 1
 
2.0%
1572 1
 
2.0%
2154 1
 
2.0%
1842 1
 
2.0%
2256 1
 
2.0%
1977 1
 
2.0%
1235 1
 
2.0%
947 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
315 1
2.0%
409 1
2.0%
435 1
2.0%
440 1
2.0%
507 1
2.0%
571 1
2.0%
576 1
2.0%
585 1
2.0%
682 1
2.0%
768 1
2.0%
ValueCountFrequency (%)
7703 1
2.0%
6896 1
2.0%
5815 1
2.0%
5682 1
2.0%
5485 1
2.0%
4712 1
2.0%
4627 1
2.0%
3906 1
2.0%
3622 1
2.0%
3434 1
2.0%

DPTR_HMS
Date

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:02
Maximum2022-01-01 00:59:26
2023-12-10T23:44:13.631897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:13.760945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)

ARVL_HMS
Date

Distinct33
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-07-17 21:41:47
Maximum2022-07-17 22:00:21
2023-12-10T23:44:13.904772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:14.023514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

FRGHT_CNVNC_QTY_TONM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0220926 × 108
Minimum16270300
Maximum4.95679 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:44:14.177760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16270300
5-th percentile17512920
Q129165700
median79282700
Q31.2254 × 108
95-th percentile2.946036 × 108
Maximum4.95679 × 108
Range4.794087 × 108
Interquartile range (IQR)93374300

Descriptive statistics

Standard deviation97447950
Coefficient of variation (CV)0.95341611
Kurtosis5.6350466
Mean1.0220926 × 108
Median Absolute Deviation (MAD)50117000
Skewness2.1442017
Sum5.0082535 × 109
Variance9.496103 × 1015
MonotonicityStrictly decreasing
2023-12-10T23:44:14.327425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
495679000 1
 
2.0%
28848100 1
 
2.0%
74541000 1
 
2.0%
73136000 1
 
2.0%
66147600 1
 
2.0%
56823400 1
 
2.0%
55601200 1
 
2.0%
54539800 1
 
2.0%
39129600 1
 
2.0%
38309400 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
16270300 1
2.0%
17139200 1
2.0%
17391200 1
2.0%
17695500 1
2.0%
17715300 1
2.0%
18667300 1
2.0%
18915700 1
2.0%
19810300 1
2.0%
21315300 1
2.0%
22841900 1
2.0%
ValueCountFrequency (%)
495679000 1
2.0%
372632000 1
2.0%
340668000 1
2.0%
225507000 1
2.0%
196745000 1
2.0%
194357000 1
2.0%
187156000 1
2.0%
185124000 1
2.0%
177430000 1
2.0%
162292000 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
2023-12-10T23:44:14.496316image/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
2023-12-10T23:44:14.681742image/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

2023-12-10T23:44:11.703128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:10.377275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.030473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.387628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.779258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:10.788167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.114239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.464112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.893352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:10.880868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.202889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.549150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.995376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:10.955521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.307908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:44:11.627508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:44:14.808331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
RANK1.0001.0000.8450.7590.3990.8081.000
DPTR_CN_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.8451.0001.0000.0000.0000.9160.845
DPTR_HMS0.7591.0000.0001.0000.9290.0000.759
ARVL_HMS0.3991.0000.0000.9291.0000.0000.399
FRGHT_CNVNC_QTY_TONM0.8081.0000.9160.0000.0001.0000.808
RN1.0001.0000.8450.7590.3990.8081.000
2023-12-10T23:44:14.970264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTY_TONMRN
RANK1.000-0.936-1.0001.000
SHIP_CNT-0.9361.0000.936-0.936
FRGHT_CNVNC_QTY_TONM-1.0000.9361.000-1.000
RN1.000-0.936-1.0001.000

Missing values

2023-12-10T23:44:12.106104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:44:12.222849image/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_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
02Australia548501-Jan-2022 00:00:3417-Jul-2022 22:00:084956790002
13Malaysia689601-Jan-2022 00:01:4317-Jul-2022 21:59:593726320003
24China770301-Jan-2022 00:04:5117-Jul-2022 22:00:183406680004
35Spain581501-Jan-2022 00:00:1517-Jul-2022 21:59:392255070005
46Taiwan390601-Jan-2022 00:01:1017-Jul-2022 21:59:441967450006
57United States568201-Jan-2022 00:00:3317-Jul-2022 22:00:151943570007
68Brazil303501-Jan-2022 00:01:4517-Jul-2022 22:00:181871560008
79South Africa271001-Jan-2022 00:00:0217-Jul-2022 22:00:191851240009
810Singapore343401-Jan-2022 00:02:5517-Jul-2022 22:00:1817743000010
911France471201-Jan-2022 00:00:1117-Jul-2022 22:00:1816229200011
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
3941Germany97401-Jan-2022 00:00:1617-Jul-2022 22:00:102284190041
4042Thailand68201-Jan-2022 00:06:0017-Jul-2022 22:00:112131530042
4143Chile57601-Jan-2022 00:06:3117-Jul-2022 22:00:211981030043
4244Mauritius31501-Jan-2022 00:00:0317-Jul-2022 21:59:491891570044
4345Belgium78601-Jan-2022 00:01:4117-Jul-2022 22:00:031866730045
4446Peru44001-Jan-2022 00:06:1917-Jul-2022 21:59:471771530046
4547Qatar43501-Jan-2022 00:55:5417-Jul-2022 22:00:191769550047
4648Gibraltar40901-Jan-2022 00:59:2617-Jul-2022 21:59:391739120048
4749The Bahamas50701-Jan-2022 00:45:3017-Jul-2022 21:57:431713920049
4850Portugal58501-Jan-2022 00:00:0217-Jul-2022 21:57:361627030050