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_001300

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
DPTR_HMS has unique valuesUnique
FRGHT_CNVNC_QTY_TONM has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:40:42.192771
Analysis finished2023-12-10 14:40:43.632269
Duration1.44 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
2023-12-10T23:40:43.691936image/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
2023-12-10T23:40:43.799137image/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
2023-12-10T23:40:43.973579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length7.4693878
Min length4

Characters and Unicode

Total characters366
Distinct characters46
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 rowNetherlands
ValueCountFrequency (%)
united 3
 
5.3%
south 2
 
3.5%
gibraltar 1
 
1.8%
thailand 1
 
1.8%
sri 1
 
1.8%
lanka 1
 
1.8%
brazil 1
 
1.8%
sweden 1
 
1.8%
colombia 1
 
1.8%
canada 1
 
1.8%
Other values (44) 44
77.2%
2023-12-10T23:40:44.289539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 55
15.0%
i 31
 
8.5%
n 26
 
7.1%
r 25
 
6.8%
e 25
 
6.8%
o 18
 
4.9%
t 18
 
4.9%
l 14
 
3.8%
d 14
 
3.8%
u 11
 
3.0%
Other values (36) 129
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 301
82.2%
Uppercase Letter 57
 
15.6%
Space Separator 8
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 55
18.3%
i 31
10.3%
n 26
8.6%
r 25
 
8.3%
e 25
 
8.3%
o 18
 
6.0%
t 18
 
6.0%
l 14
 
4.7%
d 14
 
4.7%
u 11
 
3.7%
Other values (14) 64
21.3%
Uppercase Letter
ValueCountFrequency (%)
S 8
14.0%
P 5
 
8.8%
I 4
 
7.0%
T 4
 
7.0%
A 4
 
7.0%
M 4
 
7.0%
E 3
 
5.3%
N 3
 
5.3%
U 3
 
5.3%
K 3
 
5.3%
Other values (11) 16
28.1%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 358
97.8%
Common 8
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 55
15.4%
i 31
 
8.7%
n 26
 
7.3%
r 25
 
7.0%
e 25
 
7.0%
o 18
 
5.0%
t 18
 
5.0%
l 14
 
3.9%
d 14
 
3.9%
u 11
 
3.1%
Other values (35) 121
33.8%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 55
15.0%
i 31
 
8.5%
n 26
 
7.1%
r 25
 
6.8%
e 25
 
6.8%
o 18
 
4.9%
t 18
 
4.9%
l 14
 
3.8%
d 14
 
3.8%
u 11
 
3.0%
Other values (36) 129
35.2%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.4694
Minimum539
Maximum10806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:40:44.399218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum539
5-th percentile637
Q11073
median1783
Q33294
95-th percentile6594
Maximum10806
Range10267
Interquartile range (IQR)2221

Descriptive statistics

Standard deviation2139.0909
Coefficient of variation (CV)0.83706377
Kurtosis3.6614916
Mean2555.4694
Median Absolute Deviation (MAD)900
Skewness1.7530975
Sum125218
Variance4575709.7
MonotonicityNot monotonic
2023-12-10T23:40:44.497952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1286 2
 
4.1%
664 2
 
4.1%
2576 2
 
4.1%
1073 1
 
2.0%
1149 1
 
2.0%
1701 1
 
2.0%
1199 1
 
2.0%
1116 1
 
2.0%
1458 1
 
2.0%
1076 1
 
2.0%
Other values (36) 36
73.5%
ValueCountFrequency (%)
539 1
2.0%
570 1
2.0%
619 1
2.0%
664 2
4.1%
711 1
2.0%
724 1
2.0%
734 1
2.0%
883 1
2.0%
925 1
2.0%
930 1
2.0%
ValueCountFrequency (%)
10806 1
2.0%
7278 1
2.0%
6842 1
2.0%
6222 1
2.0%
5813 1
2.0%
5607 1
2.0%
5156 1
2.0%
4276 1
2.0%
3885 1
2.0%
3873 1
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 00:19:38
Maximum2023-01-02 16:03:56
2023-12-10T23:40:44.591030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:44.693472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Categorical

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
31-May-2023 23:59:59
10 
31-May-2023 23:59:58
7 
31-May-2023 23:59:50
4 
31-May-2023 23:59:57
3 
31-May-2023 23:59:47
 
2
Other values (18)
23 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique13 ?
Unique (%)26.5%

Sample

1st row31-May-2023 23:59:59
2nd row31-May-2023 23:59:59
3rd row31-May-2023 23:59:58
4th row31-May-2023 23:59:59
5th row31-May-2023 23:59:57

Common Values

ValueCountFrequency (%)
31-May-2023 23:59:59 10
20.4%
31-May-2023 23:59:58 7
14.3%
31-May-2023 23:59:50 4
 
8.2%
31-May-2023 23:59:57 3
 
6.1%
31-May-2023 23:59:47 2
 
4.1%
31-May-2023 23:59:46 2
 
4.1%
31-May-2023 23:59:56 2
 
4.1%
31-May-2023 23:59:54 2
 
4.1%
31-May-2023 23:59:53 2
 
4.1%
31-May-2023 23:59:55 2
 
4.1%
Other values (13) 13
26.5%

Length

2023-12-10T23:40:44.817448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
31-may-2023 49
50.0%
23:59:59 10
 
10.2%
23:59:58 7
 
7.1%
23:59:50 4
 
4.1%
23:59:57 3
 
3.1%
23:59:53 2
 
2.0%
23:59:55 2
 
2.0%
23:59:54 2
 
2.0%
23:59:56 2
 
2.0%
23:59:46 2
 
2.0%
Other values (14) 15
 
15.3%

FRGHT_CNVNC_QTY_TONM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19262441
Minimum5329920
Maximum84184500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:40:44.924743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5329920
5-th percentile5428170
Q17937270
median13980500
Q324959700
95-th percentile49391480
Maximum84184500
Range78854580
Interquartile range (IQR)17022430

Descriptive statistics

Standard deviation16073452
Coefficient of variation (CV)0.83444523
Kurtosis4.4733455
Mean19262441
Median Absolute Deviation (MAD)7944560
Skewness1.8626613
Sum9.4385962 × 108
Variance2.5835586 × 1014
MonotonicityStrictly decreasing
2023-12-10T23:40:45.067310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
84184500 1
 
2.0%
7838130 1
 
2.0%
10340900 1
 
2.0%
10240000 1
 
2.0%
9812940 1
 
2.0%
9181540 1
 
2.0%
8765470 1
 
2.0%
8442720 1
 
2.0%
8426480 1
 
2.0%
8044530 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
5329920 1
2.0%
5345330 1
2.0%
5404130 1
2.0%
5464230 1
2.0%
5628640 1
2.0%
5733110 1
2.0%
5887850 1
2.0%
5979360 1
2.0%
6035940 1
2.0%
6052260 1
2.0%
ValueCountFrequency (%)
84184500 1
2.0%
52051300 1
2.0%
50319400 1
2.0%
47999600 1
2.0%
41265700 1
2.0%
41250800 1
2.0%
36324700 1
2.0%
33847900 1
2.0%
33005700 1
2.0%
27998700 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
2023-12-10T23:40:45.178552image/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
2023-12-10T23:40:45.283731image/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

2023-12-10T23:40:43.231786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:42.444131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:42.692132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:42.953995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:43.297688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:42.504533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:42.754703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:43.023118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:43.358317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:42.562638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:42.815892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:43.087685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:43.427840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:42.632896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:42.889055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:40:43.163902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:40:45.357987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
RANK1.0001.0000.8041.0000.1120.8361.000
DPTR_CN_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.8041.0001.0001.0000.0000.9240.804
DPTR_HMS1.0001.0001.0001.0001.0001.0001.000
ARVL_HMS0.1121.0000.0001.0001.0000.0000.112
FRGHT_CNVNC_QTY_TONM0.8361.0000.9241.0000.0001.0000.836
RN1.0001.0000.8041.0000.1120.8361.000
2023-12-10T23:40:45.444093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTY_TONMRNARVL_HMS
RANK1.000-0.981-1.0001.0000.000
SHIP_CNT-0.9811.0000.981-0.9810.000
FRGHT_CNVNC_QTY_TONM-1.0000.9811.000-1.0000.000
RN1.000-0.981-1.0001.0000.000
ARVL_HMS0.0000.0000.0000.0001.000

Missing values

2023-12-10T23:40:43.511864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:40:43.597996image/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
02Singapore1080601-Jan-2023 00:48:1631-May-2023 23:59:59841845002
13South Korea727801-Jan-2023 01:46:1431-May-2023 23:59:59520513003
24Malaysia684201-Jan-2023 01:22:1531-May-2023 23:59:58503194004
35United States581301-Jan-2023 00:28:0831-May-2023 23:59:59479996005
46Netherlands622201-Jan-2023 04:36:5031-May-2023 23:59:57412657006
57Spain560701-Jan-2023 03:02:5731-May-2023 23:59:55412508007
68Egypt388501-Jan-2023 03:33:3931-May-2023 23:59:58363247008
79United Arab Emirates387301-Jan-2023 02:17:2131-May-2023 23:59:53338479009
810Japan515601-Jan-2023 01:45:4131-May-2023 23:59:593300570010
911United Kingdom427601-Jan-2023 02:15:4831-May-2023 23:59:502799870011
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
3941Iraq57002-Jan-2023 10:35:4231-May-2023 23:59:48605226041
4042Nigeria72401-Jan-2023 05:13:4131-May-2023 23:59:59603594042
4143Poland92501-Jan-2023 17:52:2131-May-2023 23:59:46597936043
4244Finland93001-Jan-2023 08:56:1731-May-2023 23:59:35588785044
4345Peru71101-Jan-2023 15:37:0631-May-2023 23:59:19573311045
4446South Africa66401-Jan-2023 09:47:5331-May-2023 23:59:54562864046
4547Togo73402-Jan-2023 16:03:5631-May-2023 23:59:58546423047
4648Kuwait53901-Jan-2023 06:45:1431-May-2023 23:59:58540413048
4749Ecuador66401-Jan-2023 00:29:2031-May-2023 23:59:47534533049
4850Qatar61901-Jan-2023 02:09:2531-May-2023 23:59:33532992050