Overview

Dataset statistics

Number of variables9
Number of observations49
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory80.7 B

Variable types

Numeric6
Text1
Categorical2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001103

Alerts

RANK is highly overall correlated with PRFMC and 1 other fieldsHigh correlation
SHIP_CNT is highly overall correlated with FUEL_CNSMP_QTY and 1 other fieldsHigh correlation
PRFMC is highly overall correlated with RANK and 1 other fieldsHigh correlation
FUEL_CNSMP_QTY is highly overall correlated with SHIP_CNT and 1 other fieldsHigh correlation
NVGTN_DIST is highly overall correlated with SHIP_CNT and 1 other fieldsHigh correlation
RN is highly overall correlated with RANK and 1 other fieldsHigh correlation
DPTR_HMS is highly overall correlated with ARVL_HMSHigh correlation
ARVL_HMS is highly overall correlated with DPTR_HMSHigh correlation
DPTR_HMS is highly imbalanced (56.4%)Imbalance
SHPYRD_NM has 1 (2.0%) missing valuesMissing
RANK has unique valuesUnique
PRFMC has unique valuesUnique
FUEL_CNSMP_QTY has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:21:52.689799
Analysis finished2023-12-10 14:21:56.889109
Duration4.2 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%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:56.962781image/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
2023-12-10T23:21:57.142135image/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%

SHPYRD_NM
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Memory size524.0 B
2023-12-10T23:21:57.450398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length15.645833
Min length7

Characters and Unicode

Total characters751
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st rowHalla Eng.
2nd rowLixin Shipyard
3rd rowNingbo Lantian SB
4th rowSamsung HI
5th rowSumitomo (Yokosuka)
ValueCountFrequency (%)
sb 8
 
6.9%
shipyard 7
 
6.0%
hi 4
 
3.4%
cosco 3
 
2.6%
sy 3
 
2.6%
zosen 3
 
2.6%
shin 2
 
1.7%
zhejiang 2
 
1.7%
spp 2
 
1.7%
imabari 2
 
1.7%
Other values (75) 80
69.0%
2023-12-10T23:21:57.952149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 75
 
10.0%
68
 
9.1%
n 61
 
8.1%
i 57
 
7.6%
o 36
 
4.8%
h 36
 
4.8%
S 35
 
4.7%
u 34
 
4.5%
g 28
 
3.7%
e 26
 
3.5%
Other values (48) 295
39.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 494
65.8%
Uppercase Letter 158
 
21.0%
Space Separator 68
 
9.1%
Other Punctuation 11
 
1.5%
Close Punctuation 8
 
1.1%
Open Punctuation 8
 
1.1%
Dash Punctuation 3
 
0.4%
Decimal Number 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 35
22.2%
H 12
 
7.6%
B 11
 
7.0%
I 11
 
7.0%
O 10
 
6.3%
Y 8
 
5.1%
M 8
 
5.1%
Z 8
 
5.1%
C 8
 
5.1%
J 6
 
3.8%
Other values (16) 41
25.9%
Lowercase Letter
ValueCountFrequency (%)
a 75
15.2%
n 61
12.3%
i 57
11.5%
o 36
 
7.3%
h 36
 
7.3%
u 34
 
6.9%
g 28
 
5.7%
e 26
 
5.3%
s 22
 
4.5%
r 19
 
3.8%
Other values (15) 100
20.2%
Other Punctuation
ValueCountFrequency (%)
. 10
90.9%
& 1
 
9.1%
Space Separator
ValueCountFrequency (%)
68
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 652
86.8%
Common 99
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 75
 
11.5%
n 61
 
9.4%
i 57
 
8.7%
o 36
 
5.5%
h 36
 
5.5%
S 35
 
5.4%
u 34
 
5.2%
g 28
 
4.3%
e 26
 
4.0%
s 22
 
3.4%
Other values (41) 242
37.1%
Common
ValueCountFrequency (%)
68
68.7%
. 10
 
10.1%
) 8
 
8.1%
( 8
 
8.1%
- 3
 
3.0%
2 1
 
1.0%
& 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 751
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 75
 
10.0%
68
 
9.1%
n 61
 
8.1%
i 57
 
7.6%
o 36
 
4.8%
h 36
 
4.8%
S 35
 
4.7%
u 34
 
4.5%
g 28
 
3.7%
e 26
 
3.5%
Other values (48) 295
39.3%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.53061
Minimum1
Maximum4399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:58.071558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q311
95-th percentile53.2
Maximum4399
Range4398
Interquartile range (IQR)9

Descriptive statistics

Standard deviation627.26749
Coefficient of variation (CV)6.239567
Kurtosis48.864626
Mean100.53061
Median Absolute Deviation (MAD)4
Skewness6.9860239
Sum4926
Variance393464.5
MonotonicityNot monotonic
2023-12-10T23:21:58.173986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 12
24.5%
2 8
16.3%
7 4
 
8.2%
5 3
 
6.1%
13 3
 
6.1%
9 2
 
4.1%
6 2
 
4.1%
8 2
 
4.1%
16 1
 
2.0%
18 1
 
2.0%
Other values (11) 11
22.4%
ValueCountFrequency (%)
1 12
24.5%
2 8
16.3%
3 1
 
2.0%
4 1
 
2.0%
5 3
 
6.1%
6 2
 
4.1%
7 4
 
8.2%
8 2
 
4.1%
9 2
 
4.1%
10 1
 
2.0%
ValueCountFrequency (%)
4399 1
 
2.0%
147 1
 
2.0%
54 1
 
2.0%
52 1
 
2.0%
22 1
 
2.0%
19 1
 
2.0%
18 1
 
2.0%
16 1
 
2.0%
15 1
 
2.0%
13 3
6.1%

DPTR_HMS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2022 12:00:00
38 
02-Jan-2022 00:00:00
 
3
11-Jan-2022 00:00:00
 
1
19-Jan-2022 18:00:00
 
1
14-May-2022 18:00:00
 
1
Other values (5)
5 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique8 ?
Unique (%)16.3%

Sample

1st row02-Jan-2022 00:00:00
2nd row01-Jan-2022 12:00:00
3rd row01-Jan-2022 12:00:00
4th row01-Jan-2022 12:00:00
5th row11-Jan-2022 00:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2022 12:00:00 38
77.6%
02-Jan-2022 00:00:00 3
 
6.1%
11-Jan-2022 00:00:00 1
 
2.0%
19-Jan-2022 18:00:00 1
 
2.0%
14-May-2022 18:00:00 1
 
2.0%
11-Mar-2022 18:00:00 1
 
2.0%
06-Apr-2022 18:00:00 1
 
2.0%
22-Apr-2022 12:00:00 1
 
2.0%
05-Jan-2022 12:00:00 1
 
2.0%
08-Jan-2022 06:00:00 1
 
2.0%

Length

2023-12-10T23:21:58.273821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:58.364580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12:00:00 40
40.8%
01-jan-2022 38
38.8%
00:00:00 4
 
4.1%
18:00:00 4
 
4.1%
02-jan-2022 3
 
3.1%
11-jan-2022 1
 
1.0%
19-jan-2022 1
 
1.0%
14-may-2022 1
 
1.0%
11-mar-2022 1
 
1.0%
06-apr-2022 1
 
1.0%
Other values (4) 4
 
4.1%

ARVL_HMS
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
17-Jul-2022 18:00:00
16 
05-Jun-2022 18:00:00
10 
05-Jun-2022 12:00:00
3 
17-Jul-2022 12:00:00
3 
05-Jun-2022 06:00:00
2 
Other values (12)
15 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique9 ?
Unique (%)18.4%

Sample

1st row29-Apr-2022 12:00:00
2nd row16-Jul-2022 12:00:00
3rd row05-Jun-2022 18:00:00
4th row17-Jul-2022 06:00:00
5th row16-Jul-2022 12:00:00

Common Values

ValueCountFrequency (%)
17-Jul-2022 18:00:00 16
32.7%
05-Jun-2022 18:00:00 10
20.4%
05-Jun-2022 12:00:00 3
 
6.1%
17-Jul-2022 12:00:00 3
 
6.1%
05-Jun-2022 06:00:00 2
 
4.1%
05-Jun-2022 00:00:00 2
 
4.1%
16-Jul-2022 12:00:00 2
 
4.1%
04-Jun-2022 18:00:00 2
 
4.1%
17-Jul-2022 00:00:00 1
 
2.0%
17-Jul-2022 06:00:00 1
 
2.0%
Other values (7) 7
14.3%

Length

2023-12-10T23:21:58.464592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18:00:00 29
29.6%
17-jul-2022 21
21.4%
05-jun-2022 17
17.3%
12:00:00 10
 
10.2%
06:00:00 6
 
6.1%
00:00:00 4
 
4.1%
16-jul-2022 3
 
3.1%
04-jun-2022 2
 
2.0%
15-jul-2022 1
 
1.0%
14-jul-2022 1
 
1.0%
Other values (4) 4
 
4.1%

PRFMC
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2547637 × 108
Minimum2.45287 × 108
Maximum3.6442 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:58.567608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.45287 × 108
5-th percentile2.684426 × 108
Q13.09267 × 108
median3.29598 × 108
Q33.50703 × 108
95-th percentile3.617228 × 108
Maximum3.6442 × 108
Range1.19133 × 108
Interquartile range (IQR)41436000

Descriptive statistics

Standard deviation30285288
Coefficient of variation (CV)0.093049115
Kurtosis0.32397998
Mean3.2547637 × 108
Median Absolute Deviation (MAD)21105000
Skewness-0.86051802
Sum1.5948342 × 1010
Variance9.1719867 × 1014
MonotonicityStrictly increasing
2023-12-10T23:21:58.699511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
245287000 1
 
2.0%
353523000 1
 
2.0%
333149000 1
 
2.0%
337103000 1
 
2.0%
337225000 1
 
2.0%
343417000 1
 
2.0%
344519000 1
 
2.0%
345872000 1
 
2.0%
348283000 1
 
2.0%
348898000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
245287000 1
2.0%
247373000 1
2.0%
267053000 1
2.0%
270527000 1
2.0%
277147000 1
2.0%
290237000 1
2.0%
296603000 1
2.0%
298031000 1
2.0%
300139000 1
2.0%
300396000 1
2.0%
ValueCountFrequency (%)
364420000 1
2.0%
362648000 1
2.0%
362390000 1
2.0%
360722000 1
2.0%
360529000 1
2.0%
359226000 1
2.0%
356982000 1
2.0%
355062000 1
2.0%
354615000 1
2.0%
354455000 1
2.0%

FUEL_CNSMP_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1462961 × 1011
Minimum1.53575 × 109
Maximum2.54653 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:58.823308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.53575 × 109
5-th percentile3.763424 × 109
Q11.08685 × 1010
median3.81494 × 1010
Q31.01574 × 1011
95-th percentile4.657482 × 1011
Maximum2.54653 × 1013
Range2.5463764 × 1013
Interquartile range (IQR)9.07055 × 1010

Descriptive statistics

Standard deviation3.6306727 × 1012
Coefficient of variation (CV)5.9070904
Kurtosis48.620785
Mean6.1462961 × 1011
Median Absolute Deviation (MAD)3.127302 × 1010
Skewness6.9613294
Sum3.0116851 × 1013
Variance1.3181784 × 1025
MonotonicityNot monotonic
2023-12-10T23:21:59.168766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1754960000 1
 
2.0%
38149400000 1
 
2.0%
14160800000 1
 
2.0%
10634800000 1
 
2.0%
119337000000 1
 
2.0%
486835000000 1
 
2.0%
81378400000 1
 
2.0%
434118000000 1
 
2.0%
141063000000 1
 
2.0%
62071000000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1535750000 1
2.0%
1754960000 1
2.0%
3357820000 1
2.0%
4371830000 1
2.0%
4399240000 1
2.0%
4705290000 1
2.0%
5187540000 1
2.0%
5260400000 1
2.0%
6876380000 1
2.0%
7980580000 1
2.0%
ValueCountFrequency (%)
25465300000000 1
2.0%
1446650000000 1
2.0%
486835000000 1
2.0%
434118000000 1
2.0%
224215000000 1
2.0%
162983000000 1
2.0%
151120000000 1
2.0%
141063000000 1
2.0%
120571000000 1
2.0%
119818000000 1
2.0%

NVGTN_DIST
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.091
Minimum5.1778
Maximum84845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:59.356715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.1778
5-th percentile13.89982
Q132.544
median114.97
Q3297.609
95-th percentile1352.628
Maximum84845
Range84839.822
Interquartile range (IQR)265.065

Descriptive statistics

Standard deviation12096.554
Coefficient of variation (CV)6.0239074
Kurtosis48.721605
Mean2008.091
Median Absolute Deviation (MAD)93.1555
Skewness6.9714929
Sum98396.457
Variance1.4632662 × 108
MonotonicityNot monotonic
2023-12-10T23:21:59.513308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
7.15472 1
 
2.0%
107.912 1
 
2.0%
42.5058 1
 
2.0%
31.5476 1
 
2.0%
353.879 1
 
2.0%
1417.62 1
 
2.0%
236.209 1
 
2.0%
1255.14 1
 
2.0%
405.023 1
 
2.0%
177.906 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
5.1778 1
2.0%
7.15472 1
2.0%
13.5739 1
2.0%
14.3887 1
2.0%
15.1574 1
2.0%
16.1604 1
2.0%
17.6193 1
2.0%
17.6505 1
2.0%
21.8145 1
2.0%
25.226 1
2.0%
ValueCountFrequency (%)
84845.0 1
2.0%
4124.99 1
2.0%
1417.62 1
2.0%
1255.14 1
2.0%
633.045 1
2.0%
456.559 1
2.0%
426.344 1
2.0%
405.023 1
2.0%
365.813 1
2.0%
353.879 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
2023-12-10T23:21:59.643956image/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
2023-12-10T23:21:59.788825image/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

2023-12-10T23:21:56.100139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:53.049434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:53.825165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:54.376697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.010834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.566021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:56.180698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:53.118267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:53.901275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:54.496604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.115342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.642473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:56.259382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:53.187178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:53.988178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:54.591212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.192819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.736159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:56.357686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:53.260024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:54.071326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:54.675124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.284947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.829094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:56.442613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:53.332333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:54.165399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:54.787032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.375201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.934536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:56.543064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:53.461019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:54.273920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:54.905583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:55.473330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:56.023932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:21:59.892255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
RANK1.0001.0000.0000.0000.4430.9690.3120.0001.000
SHPYRD_NM1.0001.000NaN1.0001.0001.000NaNNaN1.000
SHIP_CNT0.000NaN1.0000.0000.0000.3120.6760.6770.000
DPTR_HMS0.0001.0000.0001.0000.8670.0000.0000.0000.000
ARVL_HMS0.4431.0000.0000.8671.0000.3530.0000.0000.443
PRFMC0.9691.0000.3120.0000.3531.0000.5150.3120.969
FUEL_CNSMP_QTY0.312NaN0.6760.0000.0000.5151.0000.6760.312
NVGTN_DIST0.000NaN0.6770.0000.0000.3120.6761.0000.000
RN1.0001.0000.0000.0000.4430.9690.3120.0001.000
2023-12-10T23:22:00.010376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ARVL_HMSDPTR_HMS
ARVL_HMS1.0000.516
DPTR_HMS0.5161.000
2023-12-10T23:22:00.117220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RANKSHIP_CNTPRFMCFUEL_CNSMP_QTYNVGTN_DISTRNDPTR_HMSARVL_HMS
RANK1.0000.3721.0000.4640.4311.0000.0000.129
SHIP_CNT0.3721.0000.3720.9780.9790.3720.0000.000
PRFMC1.0000.3721.0000.4640.4311.0000.0000.163
FUEL_CNSMP_QTY0.4640.9780.4641.0000.9980.4640.0000.000
NVGTN_DIST0.4310.9790.4310.9981.0000.4310.0000.000
RN1.0000.3721.0000.4640.4311.0000.0000.129
DPTR_HMS0.0000.0000.0000.0000.0000.0001.0000.516
ARVL_HMS0.1290.0000.1630.0000.0000.1290.5161.000

Missing values

2023-12-10T23:21:56.659743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:21:56.824891image/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

RANKSHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
02Halla Eng.102-Jan-2022 00:00:0029-Apr-2022 12:00:0024528700017549600007.154722
13Lixin Shipyard101-Jan-2022 12:00:0016-Jul-2022 12:00:00247373000335782000013.57393
24Ningbo Lantian SB101-Jan-2022 12:00:0005-Jun-2022 18:00:00267053000470529000017.61934
35Samsung HI101-Jan-2022 12:00:0017-Jul-2022 06:00:00270527000437183000016.16045
46Sumitomo (Yokosuka)611-Jan-2022 00:00:0016-Jul-2022 12:00:002771470002587080000093.34716
57EISA - Estaleiro101-Jan-2022 12:00:0017-Jul-2022 18:00:00290237000439924000015.15747
68IVI (Rio de Janeiro)102-Jan-2022 00:00:0005-Jun-2022 12:00:0029660300015357500005.17788
79Taizhou Maple Leaf101-Jan-2022 12:00:0005-Jun-2022 12:00:00298031000526040000017.65059
810<NA>439901-Jan-2022 12:00:0005-Jun-2022 18:00:003001390002546530000000084845.010
911Qingdao Wuchuan HI401-Jan-2022 12:00:0015-Jul-2022 00:00:0030039600041619300000138.54811
RANKSHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
3941CMJL (Nanjing)1501-Jan-2022 12:00:0005-Jun-2022 00:00:00354455000151120000000426.34441
4042Orient Shipyard101-Jan-2022 12:00:0017-Jul-2022 18:00:003546150001154060000032.54442
4143Jiangsu Yichun S.B.301-Jan-2022 12:00:0005-Jun-2022 12:00:003550620003547980000099.925743
4244COSCO Dalian SY1805-Jan-2022 12:00:0005-May-2022 06:00:00356982000162983000000456.55944
4345SPP Sacheon SY1301-Jan-2022 12:00:0017-Jul-2022 12:00:00359226000119818000000333.54545
4446Fujian Crown Ocean101-Jan-2022 12:00:0020-May-2022 18:00:00360529000518754000014.388746
4547Shin Kurushima201-Jan-2022 12:00:0017-Jul-2022 18:00:003607220001934150000053.618947
4648COSCO HI (Zhoushan)601-Jan-2022 12:00:0005-Jun-2022 18:00:0036239000058416700000161.19948
4749Nantong Mingde HI808-Jan-2022 06:00:0017-Jul-2022 12:00:0036264800082926400000228.66949
4850COSCO Shpg (Nantong)201-Jan-2022 12:00:0005-Jun-2022 06:00:003644200001800200000049.398950