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_000435

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

Reproduction

Analysis started2023-12-10 14:30:00.201174
Analysis finished2023-12-10 14:30:01.633718
Duration1.43 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
2023-12-10T23:30:01.688587image/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
2023-12-10T23:30:01.794316image/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
2023-12-10T23:30:01.962556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length7.3061224
Min length4

Characters and Unicode

Total characters358
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 rowMalaysia
2nd rowUnited States
3rd rowNetherlands
4th rowUnited Arab Emirates
5th rowChina
ValueCountFrequency (%)
united 3
 
5.4%
south 2
 
3.6%
thailand 1
 
1.8%
denmark 1
 
1.8%
brazil 1
 
1.8%
iraq 1
 
1.8%
morocco 1
 
1.8%
taiwan 1
 
1.8%
oman 1
 
1.8%
kuwait 1
 
1.8%
Other values (43) 43
76.8%
2023-12-10T23:30:02.263626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 56
15.6%
i 30
 
8.4%
n 27
 
7.5%
e 24
 
6.7%
r 23
 
6.4%
t 19
 
5.3%
o 17
 
4.7%
l 15
 
4.2%
d 13
 
3.6%
g 9
 
2.5%
Other values (36) 125
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 295
82.4%
Uppercase Letter 56
 
15.6%
Space Separator 7
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 56
19.0%
i 30
10.2%
n 27
9.2%
e 24
 
8.1%
r 23
 
7.8%
t 19
 
6.4%
o 17
 
5.8%
l 15
 
5.1%
d 13
 
4.4%
g 9
 
3.1%
Other values (14) 62
21.0%
Uppercase Letter
ValueCountFrequency (%)
A 7
12.5%
S 6
 
10.7%
I 4
 
7.1%
T 4
 
7.1%
M 4
 
7.1%
K 3
 
5.4%
P 3
 
5.4%
U 3
 
5.4%
N 3
 
5.4%
G 3
 
5.4%
Other values (11) 16
28.6%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 351
98.0%
Common 7
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 56
16.0%
i 30
 
8.5%
n 27
 
7.7%
e 24
 
6.8%
r 23
 
6.6%
t 19
 
5.4%
o 17
 
4.8%
l 15
 
4.3%
d 13
 
3.7%
g 9
 
2.6%
Other values (35) 118
33.6%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 56
15.6%
i 30
 
8.4%
n 27
 
7.5%
e 24
 
6.7%
r 23
 
6.4%
t 19
 
5.3%
o 17
 
4.7%
l 15
 
4.2%
d 13
 
3.6%
g 9
 
2.5%
Other values (36) 125
34.9%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1127.0612
Minimum259
Maximum3765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:30:02.375884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum259
5-th percentile266.8
Q1446
median886
Q31429
95-th percentile2906.2
Maximum3765
Range3506
Interquartile range (IQR)983

Descriptive statistics

Standard deviation893.60992
Coefficient of variation (CV)0.79286724
Kurtosis1.0624451
Mean1127.0612
Median Absolute Deviation (MAD)478
Skewness1.3057511
Sum55226
Variance798538.68
MonotonicityNot monotonic
2023-12-10T23:30:02.490564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3765 1
 
2.0%
510 1
 
2.0%
449 1
 
2.0%
760 1
 
2.0%
708 1
 
2.0%
549 1
 
2.0%
395 1
 
2.0%
578 1
 
2.0%
525 1
 
2.0%
446 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
259 1
2.0%
264 1
2.0%
266 1
2.0%
268 1
2.0%
290 1
2.0%
334 1
2.0%
335 1
2.0%
340 1
2.0%
365 1
2.0%
395 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
2023-12-10T23:30:02.592120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:02.700204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Date

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-05-31 23:58:57
Maximum2023-05-31 23:59:59
2023-12-10T23:30:02.797436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:02.886424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

FRGHT_CNVNC_QTY_TONM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8166043.7
Minimum2064760
Maximum25283100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:30:02.985129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2064760
5-th percentile2145188
Q13237360
median6144340
Q39666570
95-th percentile21268780
Maximum25283100
Range23218340
Interquartile range (IQR)6429210

Descriptive statistics

Standard deviation6342653.9
Coefficient of variation (CV)0.77671074
Kurtosis0.72572231
Mean8166043.7
Median Absolute Deviation (MAD)2932730
Skewness1.2643375
Sum4.0013614 × 108
Variance4.0229258 × 1013
MonotonicityStrictly decreasing
2023-12-10T23:30:03.088059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
25283100 1
 
2.0%
3211610 1
 
2.0%
5218480 1
 
2.0%
4692530 1
 
2.0%
4564200 1
 
2.0%
4465060 1
 
2.0%
4408320 1
 
2.0%
4236780 1
 
2.0%
4031940 1
 
2.0%
3816030 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2064760 1
2.0%
2068200 1
2.0%
2126100 1
2.0%
2173820 1
2.0%
2303540 1
2.0%
2337900 1
2.0%
2406370 1
2.0%
2739620 1
2.0%
2789640 1
2.0%
2992100 1
2.0%
ValueCountFrequency (%)
25283100 1
2.0%
24953900 1
2.0%
21441900 1
2.0%
21009100 1
2.0%
18071600 1
2.0%
17964800 1
2.0%
15699300 1
2.0%
15561100 1
2.0%
15109100 1
2.0%
14394000 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
2023-12-10T23:30:03.208498image/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
2023-12-10T23:30:03.343259image/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

2023-12-10T23:30:01.247183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:00.449320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:00.718597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:00.994311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:01.304131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:00.526374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:00.783697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:01.055808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:01.365811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:00.595802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:00.849673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:01.118665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:01.424469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:00.659196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:00.928282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:30:01.187081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:30:03.418871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
RANK1.0001.0000.7121.0000.4800.8521.000
DPTR_CN_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.7121.0001.0001.0000.0000.9050.712
DPTR_HMS1.0001.0001.0001.0001.0001.0001.000
ARVL_HMS0.4801.0000.0001.0001.0000.0000.480
FRGHT_CNVNC_QTY_TONM0.8521.0000.9051.0000.0001.0000.852
RN1.0001.0000.7121.0000.4800.8521.000
2023-12-10T23:30:03.503512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTFRGHT_CNVNC_QTY_TONMRN
RANK1.000-0.976-1.0001.000
SHIP_CNT-0.9761.0000.976-0.976
FRGHT_CNVNC_QTY_TONM-1.0000.9761.000-1.000
RN1.000-0.976-1.0001.000

Missing values

2023-12-10T23:30:01.509562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:30:01.597557image/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
02Malaysia376501-Jan-2023 02:18:5331-May-2023 23:59:50252831002
13United States295301-Jan-2023 00:51:4531-May-2023 23:59:58249539003
24Netherlands343801-Jan-2023 05:16:0931-May-2023 23:59:57214419004
35United Arab Emirates240401-Jan-2023 03:01:2131-May-2023 23:59:36210091005
46China283601-Jan-2023 00:59:3831-May-2023 23:59:56180716006
57Spain264401-Jan-2023 04:58:4731-May-2023 23:59:53179648007
68South Korea236901-Jan-2023 08:26:4831-May-2023 23:59:59156993008
79Egypt172101-Jan-2023 05:24:5031-May-2023 23:59:58155611009
810Saudi Arabia137201-Jan-2023 03:09:5931-May-2023 23:59:491510910010
911Turkey206601-Jan-2023 04:23:1831-May-2023 23:59:541439400011
RANKDPTR_CN_NMSHIP_CNTDPTR_HMSARVL_HMSFRGHT_CNVNC_QTY_TONMRN
3941South Africa36501-Jan-2023 09:47:5331-May-2023 23:59:49299210041
4042Togo42102-Jan-2023 16:03:5631-May-2023 23:59:40278964042
4143Angola26801-Jan-2023 02:29:2031-May-2023 23:59:57273962043
4244Vietnam40801-Jan-2023 06:37:0131-May-2023 23:59:36240637044
4345Libya26601-Jan-2023 11:13:5631-May-2023 23:59:35233790045
4446Portugal33401-Jan-2023 04:03:1731-May-2023 23:59:12230354046
4547Colombia26401-Jan-2023 12:06:3231-May-2023 23:59:30217382047
4648Algeria25901-Jan-2023 13:14:0331-May-2023 23:58:57212610048
4749Argentina29001-Jan-2023 19:25:3031-May-2023 23:59:38206820049
4850Poland33502-Jan-2023 11:18:1031-May-2023 23:59:46206476050