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
DateTime1
Categorical1

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000444

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 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
SHIP_CNT has unique valuesUnique
DPTR_HMS has unique valuesUnique
FRGHT_CNVNC_QTY has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:38:50.444176
Analysis finished2023-12-10 14:38:52.042077
Duration1.6 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
2023-12-10T23:38:52.107268image/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
2023-12-10T23:38:52.278166image/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

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:38:52.474710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length16
Mean length8.7959184
Min length4

Characters and Unicode

Total characters431
Distinct characters44
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

Unique24 ?
Unique (%)49.0%

Sample

1st rowCosta Rica
2nd rowIraq
3rd rowUnited States
4th rowEgypt
5th rowSingapore
ValueCountFrequency (%)
netherlands 6
 
9.4%
united 6
 
9.4%
states 5
 
7.8%
belgium 4
 
6.2%
china 3
 
4.7%
spain 3
 
4.7%
namibia 2
 
3.1%
singapore 2
 
3.1%
libya 1
 
1.6%
papua 1
 
1.6%
Other values (31) 31
48.4%
2023-12-10T23:38:52.785804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 50
 
11.6%
e 45
 
10.4%
i 42
 
9.7%
t 32
 
7.4%
n 32
 
7.4%
r 22
 
5.1%
s 18
 
4.2%
u 15
 
3.5%
l 15
 
3.5%
15
 
3.5%
Other values (34) 145
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 352
81.7%
Uppercase Letter 63
 
14.6%
Space Separator 15
 
3.5%
Other Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 50
14.2%
e 45
12.8%
i 42
11.9%
t 32
9.1%
n 32
9.1%
r 22
 
6.2%
s 18
 
5.1%
u 15
 
4.3%
l 15
 
4.3%
d 13
 
3.7%
Other values (13) 68
19.3%
Uppercase Letter
ValueCountFrequency (%)
S 12
19.0%
N 10
15.9%
U 7
11.1%
C 5
7.9%
B 4
 
6.3%
I 3
 
4.8%
G 3
 
4.8%
M 3
 
4.8%
P 2
 
3.2%
L 2
 
3.2%
Other values (9) 12
19.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 415
96.3%
Common 16
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 50
 
12.0%
e 45
 
10.8%
i 42
 
10.1%
t 32
 
7.7%
n 32
 
7.7%
r 22
 
5.3%
s 18
 
4.3%
u 15
 
3.6%
l 15
 
3.6%
d 13
 
3.1%
Other values (32) 131
31.6%
Common
ValueCountFrequency (%)
15
93.8%
, 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 50
 
11.6%
e 45
 
10.4%
i 42
 
9.7%
t 32
 
7.4%
n 32
 
7.4%
r 22
 
5.1%
s 18
 
4.2%
u 15
 
3.5%
l 15
 
3.5%
15
 
3.5%
Other values (34) 145
33.6%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1108.2041
Minimum86
Maximum3765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:38:52.901970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile192.8
Q1421
median886
Q31429
95-th percentile2906.2
Maximum3765
Range3679
Interquartile range (IQR)1008

Descriptive statistics

Standard deviation911.54774
Coefficient of variation (CV)0.82254502
Kurtosis0.92613267
Mean1108.2041
Median Absolute Deviation (MAD)491
Skewness1.236603
Sum54302
Variance830919.29
MonotonicityNot monotonic
2023-12-10T23:38:53.015510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2953 1
 
2.0%
264 1
 
2.0%
549 1
 
2.0%
893 1
 
2.0%
1022 1
 
2.0%
525 1
 
2.0%
708 1
 
2.0%
340 1
 
2.0%
1000 1
 
2.0%
1109 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
86 1
2.0%
117 1
2.0%
180 1
2.0%
212 1
2.0%
259 1
2.0%
264 1
2.0%
268 1
2.0%
290 1
2.0%
334 1
2.0%
340 1
2.0%
ValueCountFrequency (%)
3765 1
2.0%
3438 1
2.0%
2953 1
2.0%
2836 1
2.0%
2644 1
2.0%
2404 1
2.0%
2369 1
2.0%
2066 1
2.0%
2060 1
2.0%
1721 1
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 00:51:45
Maximum2023-01-02 16:03:56
2023-12-10T23:38:53.128769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:53.514101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Categorical

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
31-May-2023 23:59:59
5 
31-May-2023 23:59:57
4 
31-May-2023 23:59:58
4 
31-May-2023 23:59:49
 
3
31-May-2023 23:59:56
 
3
Other values (19)
30 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique10 ?
Unique (%)20.4%

Sample

1st row31-May-2023 23:59:58
2nd row31-May-2023 23:59:36
3rd row31-May-2023 23:59:58
4th row31-May-2023 23:59:49
5th row31-May-2023 23:59:56

Common Values

ValueCountFrequency (%)
31-May-2023 23:59:59 5
 
10.2%
31-May-2023 23:59:57 4
 
8.2%
31-May-2023 23:59:58 4
 
8.2%
31-May-2023 23:59:49 3
 
6.1%
31-May-2023 23:59:56 3
 
6.1%
31-May-2023 23:59:50 3
 
6.1%
31-May-2023 23:59:54 3
 
6.1%
31-May-2023 23:59:40 2
 
4.1%
31-May-2023 23:59:52 2
 
4.1%
31-May-2023 23:59:48 2
 
4.1%
Other values (14) 18
36.7%

Length

2023-12-10T23:38:53.626696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
31-may-2023 49
50.0%
23:59:59 5
 
5.1%
23:59:57 4
 
4.1%
23:59:58 4
 
4.1%
23:59:49 3
 
3.1%
23:59:56 3
 
3.1%
23:59:50 3
 
3.1%
23:59:54 3
 
3.1%
23:59:53 2
 
2.0%
23:59:43 2
 
2.0%
Other values (15) 20
20.4%

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6068 × 1010
Minimum3.01914 × 109
Maximum8.37556 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:38:53.729450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.01914 × 109
5-th percentile3.62458 × 109
Q15.40256 × 109
median9.80104 × 109
Q32.16325 × 1010
95-th percentile4.838696 × 1010
Maximum8.37556 × 1010
Range8.073646 × 1010
Interquartile range (IQR)1.622994 × 1010

Descriptive statistics

Standard deviation1.5993953 × 1010
Coefficient of variation (CV)0.99539163
Kurtosis6.1062898
Mean1.6068 × 1010
Median Absolute Deviation (MAD)5.45218 × 109
Skewness2.2363337
Sum7.87332 × 1011
Variance2.5580653 × 1020
MonotonicityStrictly decreasing
2023-12-10T23:38:53.860182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
83755600000 1
 
2.0%
5231410000 1
 
2.0%
8431800000 1
 
2.0%
8198330000 1
 
2.0%
7831660000 1
 
2.0%
7713220000 1
 
2.0%
7475300000 1
 
2.0%
6705930000 1
 
2.0%
6650190000 1
 
2.0%
6498290000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3019140000 1
2.0%
3328240000 1
2.0%
3578400000 1
2.0%
3693850000 1
2.0%
4139170000 1
2.0%
4192790000 1
2.0%
4241940000 1
2.0%
4348860000 1
2.0%
4373800000 1
2.0%
4405060000 1
2.0%
ValueCountFrequency (%)
83755600000 1
2.0%
53549100000 1
2.0%
48597600000 1
2.0%
48071000000 1
2.0%
35681200000 1
2.0%
32509600000 1
2.0%
30399800000 1
2.0%
28897700000 1
2.0%
27626600000 1
2.0%
25172500000 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
2023-12-10T23:38:54.019993image/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
2023-12-10T23:38:54.168252image/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

2023-12-10T23:38:51.597358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:50.703154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:50.988400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.289481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.662917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:50.766936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.057513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.365793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.734901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:50.843980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.133075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.445937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.811873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:50.920982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.213350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:38:51.525260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:38:54.247979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
RANK1.0000.4190.5691.0000.4760.7981.000
DPTR_CN_NM0.4191.0000.0001.0000.0000.7610.419
SHIP_CNT0.5690.0001.0001.0000.0000.7210.569
DPTR_HMS1.0001.0001.0001.0001.0001.0001.000
ARVL_HMS0.4760.0000.0001.0001.0000.0000.476
FRGHT_CNVNC_QTY0.7980.7610.7211.0000.0001.0000.798
RN1.0000.4190.5691.0000.4760.7981.000
2023-12-10T23:38:54.339614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRNARVL_HMS
RANK1.000-0.732-1.0001.0000.215
SHIP_CNT-0.7321.0000.732-0.7320.000
FRGHT_CNVNC_QTY-1.0000.7321.000-1.0000.000
RN1.000-0.732-1.0001.0000.215
ARVL_HMS0.2150.0000.0000.2151.000

Missing values

2023-12-10T23:38:51.904687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:38:52.005558image/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_QTYRN
02Costa Rica295301-Jan-2023 00:51:4531-May-2023 23:59:58837556000002
13Iraq240401-Jan-2023 03:01:2131-May-2023 23:59:36535491000003
24United States172101-Jan-2023 05:24:5031-May-2023 23:59:58485976000004
35Egypt137201-Jan-2023 03:09:5931-May-2023 23:59:49480710000005
46Singapore283601-Jan-2023 00:59:3831-May-2023 23:59:56356812000006
57United States236901-Jan-2023 08:26:4831-May-2023 23:59:59325096000007
68Saint Helena, Ascension en Tri376501-Jan-2023 02:18:5331-May-2023 23:59:50303998000008
79Netherlands155701-Jan-2023 10:25:0531-May-2023 23:59:57288977000009
810Chile58001-Jan-2023 09:56:2531-May-2023 23:59:582762660000010
911Belgium343801-Jan-2023 05:16:0931-May-2023 23:59:572517250000011
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
3941Uruguay29001-Jan-2023 19:25:3031-May-2023 23:59:38440506000041
4042Peru21201-Jan-2023 20:27:4131-May-2023 23:59:57437380000042
4143Italy42102-Jan-2023 16:03:5631-May-2023 23:59:40434886000043
4244Belgium33401-Jan-2023 04:03:1731-May-2023 23:59:12424194000044
4345Indonesia52101-Jan-2023 05:37:0431-May-2023 23:59:59419279000045
4446Namibia8601-Jan-2023 14:51:2131-May-2023 23:59:52413917000046
4547Namibia18001-Jan-2023 06:43:0531-May-2023 23:59:46369385000047
4648China11701-Jan-2023 09:43:4931-May-2023 23:59:49357840000048
4749Spain76001-Jan-2023 02:56:5431-May-2023 23:59:16332824000049
4850Gibraltar25901-Jan-2023 13:14:0331-May-2023 23:58:57301914000050