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_001310

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 14:46:38.092483
Analysis finished2023-12-10 14:46:39.681325
Duration1.59 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
2023-12-10T23:46:39.747905image/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
2023-12-10T23:46:39.871795image/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

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:46:40.035982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length9.0816327
Min length4

Characters and Unicode

Total characters445
Distinct characters39
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

Unique22 ?
Unique (%)44.9%

Sample

1st rowIndonesia
2nd rowColombia
3rd rowVietnam
4th rowMauritius
5th rowColombia
ValueCountFrequency (%)
united 11
 
16.2%
china 5
 
7.4%
states 5
 
7.4%
spain 3
 
4.4%
arab 3
 
4.4%
emirates 3
 
4.4%
kingdom 3
 
4.4%
peru 2
 
2.9%
colombia 2
 
2.9%
indonesia 2
 
2.9%
Other values (28) 29
42.6%
2023-12-10T23:46:40.606425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 54
 
12.1%
i 46
 
10.3%
n 39
 
8.8%
e 36
 
8.1%
t 34
 
7.6%
r 22
 
4.9%
d 20
 
4.5%
19
 
4.3%
o 17
 
3.8%
s 15
 
3.4%
Other values (29) 143
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 358
80.4%
Uppercase Letter 68
 
15.3%
Space Separator 19
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 54
15.1%
i 46
12.8%
n 39
10.9%
e 36
10.1%
t 34
9.5%
r 22
 
6.1%
d 20
 
5.6%
o 17
 
4.7%
s 15
 
4.2%
m 13
 
3.6%
Other values (10) 62
17.3%
Uppercase Letter
ValueCountFrequency (%)
U 12
17.6%
S 11
16.2%
C 10
14.7%
A 5
7.4%
P 4
 
5.9%
K 3
 
4.4%
I 3
 
4.4%
M 3
 
4.4%
E 3
 
4.4%
R 2
 
2.9%
Other values (8) 12
17.6%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 426
95.7%
Common 19
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 54
12.7%
i 46
 
10.8%
n 39
 
9.2%
e 36
 
8.5%
t 34
 
8.0%
r 22
 
5.2%
d 20
 
4.7%
o 17
 
4.0%
s 15
 
3.5%
m 13
 
3.1%
Other values (28) 130
30.5%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 54
 
12.1%
i 46
 
10.3%
n 39
 
8.8%
e 36
 
8.1%
t 34
 
7.6%
r 22
 
4.9%
d 20
 
4.5%
19
 
4.3%
o 17
 
3.8%
s 15
 
3.4%
Other values (29) 143
32.1%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1385.3061
Minimum299
Maximum5072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:40.729185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum299
5-th percentile311.2
Q1481
median957
Q31908
95-th percentile3536.6
Maximum5072
Range4773
Interquartile range (IQR)1427

Descriptive statistics

Standard deviation1181.6004
Coefficient of variation (CV)0.85295257
Kurtosis1.94725
Mean1385.3061
Median Absolute Deviation (MAD)512
Skewness1.4849674
Sum67880
Variance1396179.6
MonotonicityNot monotonic
2023-12-10T23:46:40.851736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5072 1
 
2.0%
381 1
 
2.0%
739 1
 
2.0%
935 1
 
2.0%
300 1
 
2.0%
2216 1
 
2.0%
1286 1
 
2.0%
1351 1
 
2.0%
940 1
 
2.0%
957 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
299 1
2.0%
300 1
2.0%
310 1
2.0%
313 1
2.0%
329 1
2.0%
334 1
2.0%
340 1
2.0%
381 1
2.0%
384 1
2.0%
453 1
2.0%
ValueCountFrequency (%)
5072 1
2.0%
4909 1
2.0%
3843 1
2.0%
3077 1
2.0%
2963 1
2.0%
2886 1
2.0%
2860 1
2.0%
2784 1
2.0%
2705 1
2.0%
2216 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
Maximum2023-01-04 00:51:04
2023-12-10T23:46:40.985148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:41.109351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Date

Distinct27
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-05-31 23:58:15
Maximum2023-05-31 23:59:59
2023-12-10T23:46:41.227603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:41.336641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7785801 × 1010
Minimum3.27746 × 109
Maximum1.07448 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:41.459744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.27746 × 109
5-th percentile3.615352 × 109
Q16.12336 × 109
median1.13346 × 1010
Q32.10107 × 1010
95-th percentile5.404588 × 1010
Maximum1.07448 × 1011
Range1.0417054 × 1011
Interquartile range (IQR)1.488734 × 1010

Descriptive statistics

Standard deviation1.9328232 × 1010
Coefficient of variation (CV)1.0867226
Kurtosis10.059876
Mean1.7785801 × 1010
Median Absolute Deviation (MAD)5.94024 × 109
Skewness2.8961131
Sum8.7150423 × 1011
Variance3.7358055 × 1020
MonotonicityStrictly decreasing
2023-12-10T23:46:41.583959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
107448000000 1
 
2.0%
5745970000 1
 
2.0%
10201700000 1
 
2.0%
9959920000 1
 
2.0%
9810560000 1
 
2.0%
9795220000 1
 
2.0%
9346150000 1
 
2.0%
8707250000 1
 
2.0%
8387060000 1
 
2.0%
8083870000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3277460000 1
2.0%
3507350000 1
2.0%
3601360000 1
2.0%
3636340000 1
2.0%
3737220000 1
2.0%
4274560000 1
2.0%
4490560000 1
2.0%
5195260000 1
2.0%
5271540000 1
2.0%
5394360000 1
2.0%
ValueCountFrequency (%)
107448000000 1
2.0%
72570500000 1
2.0%
61113000000 1
2.0%
43445200000 1
2.0%
37825400000 1
2.0%
34800200000 1
2.0%
31304500000 1
2.0%
29528000000 1
2.0%
23935900000 1
2.0%
21720500000 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
2023-12-10T23:46:41.706594image/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
2023-12-10T23:46:41.859273image/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

2023-12-10T23:46:39.223044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:38.364330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:38.659633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:38.935972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:39.290932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:38.444358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:38.728732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:39.006607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:39.359154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:38.513907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:38.802259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:39.072959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:39.442310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:38.587424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:38.869182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:46:39.149998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:46:41.949596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKARVL_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
RANK1.0000.6980.5391.0000.5180.7081.000
ARVL_CN_NM0.6981.0000.0001.0000.8200.4430.698
SHIP_CNT0.5390.0001.0001.0000.0000.7340.539
DPTR_HMS1.0001.0001.0001.0001.0001.0001.000
ARVL_HMS0.5180.8200.0001.0001.0000.0000.518
FRGHT_CNVNC_QTY0.7080.4430.7341.0000.0001.0000.708
RN1.0000.6980.5391.0000.5180.7081.000
2023-12-10T23:46:42.053300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTYRN
RANK1.000-0.758-1.0001.000
SHIP_CNT-0.7581.0000.758-0.758
FRGHT_CNVNC_QTY-1.0000.7581.000-1.000
RN1.000-0.758-1.0001.000

Missing values

2023-12-10T23:46:39.542440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:46:39.641471image/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_QTYRN
02Indonesia507201-Jan-2023 00:48:1631-May-2023 23:59:561074480000002
13Colombia286001-Jan-2023 00:28:0831-May-2023 23:59:59725705000003
24Vietnam490901-Jan-2023 01:46:1431-May-2023 23:59:58611130000004
35Mauritius216401-Jan-2023 03:33:3931-May-2023 23:59:57434452000005
46Colombia190801-Jan-2023 00:47:5631-May-2023 23:59:59378254000006
57Vietnam307701-Jan-2023 01:22:1531-May-2023 23:59:58348002000007
68Morocco296301-Jan-2023 03:02:5731-May-2023 23:59:55313045000008
79China384301-Jan-2023 10:27:3131-May-2023 23:59:59295280000009
810China288601-Jan-2023 02:18:3831-May-2023 23:59:302393590000010
911Qatar146901-Jan-2023 02:17:2131-May-2023 23:59:532172050000011
RANKARVL_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTYRN
3941Saudi Arabia33401-Jan-2023 09:38:5931-May-2023 23:59:31539436000041
4042Panama53001-Jan-2023 16:06:2831-May-2023 23:59:07527154000042
4143Finland81601-Jan-2023 17:49:2131-May-2023 23:59:50519526000043
4244Dominican Republic45301-Jan-2023 02:01:1531-May-2023 23:59:43449056000044
4345Peru48001-Jan-2023 00:29:2031-May-2023 23:59:45427456000045
4446Italy49801-Jan-2023 09:11:4131-May-2023 23:59:35373722000046
4547Sri Lanka38402-Jan-2023 02:13:4931-May-2023 23:59:32363634000047
4648United States47201-Jan-2023 08:27:5231-May-2023 23:59:38360136000048
4749Portugal62101-Jan-2023 11:44:0931-May-2023 23:58:15350735000049
4850Indonesia31303-Jan-2023 06:22:2431-May-2023 23:59:58327746000050