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_001081

Alerts

RANK is highly overall correlated with RNHigh correlation
SHIP_CNT is highly overall correlated with LD_RTHigh correlation
LD_RT is highly overall correlated with SHIP_CNTHigh correlation
RN is highly overall correlated with RANKHigh correlation
RANK has unique valuesUnique
SHIP_OWNER_NM has unique valuesUnique
LD_RT has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:49:48.510955
Analysis finished2023-12-10 14:49:50.743683
Duration2.23 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%
Mean290
Minimum266
Maximum314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:50.830078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum266
5-th percentile268.4
Q1278
median290
Q3302
95-th percentile311.6
Maximum314
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.049271345
Kurtosis-1.2
Mean290
Median Absolute Deviation (MAD)12
Skewness0
Sum14210
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:49:51.019890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
266 1
 
2.0%
303 1
 
2.0%
293 1
 
2.0%
294 1
 
2.0%
295 1
 
2.0%
296 1
 
2.0%
297 1
 
2.0%
298 1
 
2.0%
299 1
 
2.0%
300 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
266 1
2.0%
267 1
2.0%
268 1
2.0%
269 1
2.0%
270 1
2.0%
271 1
2.0%
272 1
2.0%
273 1
2.0%
274 1
2.0%
275 1
2.0%
ValueCountFrequency (%)
314 1
2.0%
313 1
2.0%
312 1
2.0%
311 1
2.0%
310 1
2.0%
309 1
2.0%
308 1
2.0%
307 1
2.0%
306 1
2.0%
305 1
2.0%

SHIP_OWNER_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:49:51.297167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length13.795918
Min length6

Characters and Unicode

Total characters676
Distinct characters49
Distinct categories2 ?
Distinct scripts1 ?
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 rowMruhaySgxozosk
2nd rowMuargtjxoyHxuy
3rd rowZaulaUikgt
4th rowSozyaoIu
5th rowFnkpogtmTgtdot
ValueCountFrequency (%)
mruhaysgxozosk 1
 
2.0%
lglgrouyynovvotm 1
 
2.0%
ingufnuaegzgoynvm 1
 
2.0%
uxoktzrotkiurzj 1
 
2.0%
raiqynov 1
 
2.0%
hgurosgxotkynvm 1
 
2.0%
zgocgttgbomgzout 1
 
2.0%
ygsuyyzkgsynov 1
 
2.0%
puntpxomuysgxotk 1
 
2.0%
atqtuctmxkkquctkx 1
 
2.0%
Other values (39) 39
79.6%
2023-12-10T23:49:51.762088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 76
 
11.2%
g 63
 
9.3%
t 63
 
9.3%
u 45
 
6.7%
k 38
 
5.6%
m 31
 
4.6%
n 30
 
4.4%
Y 26
 
3.8%
v 25
 
3.7%
y 25
 
3.7%
Other values (39) 254
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 552
81.7%
Uppercase Letter 124
 
18.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 76
13.8%
g 63
11.4%
t 63
11.4%
u 45
 
8.2%
k 38
 
6.9%
m 31
 
5.6%
n 30
 
5.4%
v 25
 
4.5%
y 25
 
4.5%
x 23
 
4.2%
Other values (16) 133
24.1%
Uppercase Letter
ValueCountFrequency (%)
Y 26
21.0%
I 12
9.7%
Q 11
 
8.9%
S 9
 
7.3%
R 8
 
6.5%
G 7
 
5.6%
T 7
 
5.6%
M 7
 
5.6%
Z 5
 
4.0%
P 4
 
3.2%
Other values (13) 28
22.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 676
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 76
 
11.2%
g 63
 
9.3%
t 63
 
9.3%
u 45
 
6.7%
k 38
 
5.6%
m 31
 
4.6%
n 30
 
4.4%
Y 26
 
3.8%
v 25
 
3.7%
y 25
 
3.7%
Other values (39) 254
37.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 76
 
11.2%
g 63
 
9.3%
t 63
 
9.3%
u 45
 
6.7%
k 38
 
5.6%
m 31
 
4.6%
n 30
 
4.4%
Y 26
 
3.8%
v 25
 
3.7%
y 25
 
3.7%
Other values (39) 254
37.6%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9387755
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:51.910712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile4.6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0879676
Coefficient of variation (CV)0.37021119
Kurtosis2.6956216
Mean2.9387755
Median Absolute Deviation (MAD)1
Skewness1.2387469
Sum144
Variance1.1836735
MonotonicityNot monotonic
2023-12-10T23:49:52.019815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 19
38.8%
3 16
32.7%
4 10
20.4%
5 2
 
4.1%
1 1
 
2.0%
7 1
 
2.0%
ValueCountFrequency (%)
1 1
 
2.0%
2 19
38.8%
3 16
32.7%
4 10
20.4%
5 2
 
4.1%
7 1
 
2.0%
ValueCountFrequency (%)
7 1
 
2.0%
5 2
 
4.1%
4 10
20.4%
3 16
32.7%
2 19
38.8%
1 1
 
2.0%

DPTR_HMS
Date

Distinct45
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 00:00:01
Maximum2023-03-18 03:40:30
2023-12-10T23:49:52.192438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:52.378968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)

ARVL_HMS
Date

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-03-18 08:12:30
Maximum2023-05-31 23:59:59
2023-12-10T23:49:52.562128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:52.728881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)

LD_RT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2473026
Minimum0.0818532
Maximum0.651762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:49:52.897831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0818532
5-th percentile0.1429942
Q10.173236
median0.229107
Q30.307765
95-th percentile0.3440968
Maximum0.651762
Range0.5699088
Interquartile range (IQR)0.134529

Descriptive statistics

Standard deviation0.093392095
Coefficient of variation (CV)0.377643
Kurtosis5.8780954
Mean0.2473026
Median Absolute Deviation (MAD)0.069549
Skewness1.6019134
Sum12.117827
Variance0.0087220834
MonotonicityNot monotonic
2023-12-10T23:49:53.398108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.239062 1
 
2.0%
0.199437 1
 
2.0%
0.158561 1
 
2.0%
0.211268 1
 
2.0%
0.209972 1
 
2.0%
0.156852 1
 
2.0%
0.308092 1
 
2.0%
0.307765 1
 
2.0%
0.304748 1
 
2.0%
0.304545 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0818532 1
2.0%
0.139006 1
2.0%
0.141135 1
2.0%
0.145783 1
2.0%
0.148574 1
2.0%
0.151273 1
2.0%
0.156852 1
2.0%
0.158561 1
2.0%
0.159055 1
2.0%
0.159558 1
2.0%
ValueCountFrequency (%)
0.651762 1
2.0%
0.352845 1
2.0%
0.344366 1
2.0%
0.343693 1
2.0%
0.340981 1
2.0%
0.340297 1
2.0%
0.338684 1
2.0%
0.338239 1
2.0%
0.336456 1
2.0%
0.323748 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
2023-12-10T23:49:53.555733image/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
2023-12-10T23:49:53.738686image/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

2023-12-10T23:49:50.098842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:48.887393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:49.323127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:49.719465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:50.225301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:48.976440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:49.417346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:49.800318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:50.332152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:49.092826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:49.507006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:49.911542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:50.424657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:49.227344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:49.614547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:50.006763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:49:53.835889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSLD_RTRN
RANK1.0001.0000.0000.6770.9330.4871.000
SHIP_OWNER_NM1.0001.0001.0001.0001.0001.0001.000
SHIP_CNT0.0001.0001.0000.9710.9320.9920.264
DPTR_HMS0.6771.0000.9711.0000.9710.9270.701
ARVL_HMS0.9331.0000.9320.9711.0000.8640.935
LD_RT0.4871.0000.9920.9270.8641.0000.545
RN1.0001.0000.2640.7010.9350.5451.000
2023-12-10T23:49:53.958184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTLD_RTRN
RANK1.000-0.042-0.2481.000
SHIP_CNT-0.0421.000-0.948-0.042
LD_RT-0.248-0.9481.000-0.248
RN1.000-0.042-0.2481.000

Missing values

2023-12-10T23:49:50.539478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:49:50.660335image/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

RANKSHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSLD_RTRN
0266MruhaySgxozosk301-Jan-2023 00:00:5631-May-2023 23:52:380.2390622
1267MuargtjxoyHxuy301-Jan-2023 00:01:1531-May-2023 23:59:170.2389793
2268ZaulaUikgt301-Jan-2023 00:00:1031-May-2023 23:57:400.2357644
3269SozyaoIu201-Jan-2023 00:02:1331-May-2023 23:53:380.3528455
4270FnkpogtmTgtdot501-Jan-2023 00:01:0931-May-2023 23:54:480.1411356
5271IgxgYnovvotm301-Jan-2023 00:02:0631-May-2023 23:59:590.2334147
6272AtqtuctInotkyk501-Jan-2023 00:00:2631-May-2023 23:59:530.1390068
7273GyegjYnovvotm401-Jan-2023 00:00:4131-May-2023 23:59:430.1732369
8274KoquQoykt201-Jan-2023 00:00:0131-May-2023 23:59:390.34436610
9275GvurrutogRotkyYG201-Jan-2023 00:01:1631-May-2023 23:57:270.34369311
RANKSHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSLD_RTRN
39305MrkgsxgeSgxozosk401-Jan-2023 00:00:0531-May-2023 23:59:100.14857441
40306GyogzoiRruej301-Jan-2023 00:00:2031-May-2023 23:50:220.19775442
41307LPIuxv201-Jan-2023 00:22:4931-May-2023 23:59:440.29575843
42308MagtmjutmYnovvotm301-Jan-2023 00:00:1531-May-2023 23:59:440.19504244
43309CgnQcutmYnvm401-Jan-2023 00:01:2531-May-2023 23:57:480.14578345
44310GsueygorotmSgxozosk201-Jan-2023 00:01:0631-May-2023 23:56:540.29153246
45311AtoutIusskxiogr201-Jan-2023 00:01:3431-May-2023 23:59:270.29112947
46312JgrtgbkTgb201-Jan-2023 00:00:0631-May-2023 23:57:380.28910148
47313Qutjotgbk701-Jan-2023 00:00:0231-May-2023 23:59:210.08185349
48314YamgngxgQoykt201-Jan-2023 00:00:4431-May-2023 23:59:550.28573250