Overview

Dataset statistics

Number of variables20
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory174.7 B

Variable types

Numeric9
Categorical9
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000357

Alerts

NNVGTN_TM has constant value ""Constant
DPRT_PRT_CD is highly overall correlated with MMSI and 12 other fieldsHigh correlation
ARVL_PRT_NM is highly overall correlated with MMSI and 11 other fieldsHigh correlation
ARRV_PRT_CD is highly overall correlated with MMSI and 11 other fieldsHigh correlation
DPTR_PRT_NM is highly overall correlated with MMSI and 12 other fieldsHigh correlation
MMSI is highly overall correlated with DTNT_LO and 8 other fieldsHigh correlation
DPTRP_LA is highly overall correlated with DPTRP_LO and 9 other fieldsHigh correlation
DPTRP_LO is highly overall correlated with DPTRP_LA and 9 other fieldsHigh correlation
DTNT_LA is highly overall correlated with DPTRP_LA and 8 other fieldsHigh correlation
DTNT_LO is highly overall correlated with MMSI and 10 other fieldsHigh correlation
NVGTN_TM is highly overall correlated with BLLAT_HOUR and 7 other fieldsHigh correlation
BLLAT_HOUR is highly overall correlated with NVGTN_TM and 6 other fieldsHigh correlation
FRGHT_CNVNC_TM is highly overall correlated with BLLAT_HOURHigh correlation
RN is highly overall correlated with MMSI and 2 other fieldsHigh correlation
IMO_IDNTF_NO is highly overall correlated with MMSI and 13 other fieldsHigh correlation
DPTR_CN_NM is highly overall correlated with MMSI and 12 other fieldsHigh correlation
ARVL_CN_NM is highly overall correlated with MMSI and 12 other fieldsHigh correlation
PRT_NCHRG_TM is highly imbalanced (85.6%)Imbalance
DPTR_HMS has unique valuesUnique
ARVL_HMS has unique valuesUnique
DPTRP_LA has unique valuesUnique
DPTRP_LO has unique valuesUnique
NVGTN_TM has unique valuesUnique
RN has unique valuesUnique
DPTRP_LA has 1 (2.0%) zerosZeros
DPTRP_LO has 1 (2.0%) zerosZeros
DTNT_LA has 3 (6.1%) zerosZeros
DTNT_LO has 3 (6.1%) zerosZeros
BLLAT_HOUR has 37 (75.5%) zerosZeros
FRGHT_CNVNC_TM has 13 (26.5%) zerosZeros

Reproduction

Analysis started2023-12-10 14:45:40.903649
Analysis finished2023-12-10 14:45:49.472087
Duration8.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MMSI
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0195502 × 108
Minimum2.0008208 × 108
Maximum2.05003 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:45:49.523155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0008208 × 108
5-th percentile2.0008208 × 108
Q12.0008208 × 108
median2.0136661 × 108
Q32.0399939 × 108
95-th percentile2.05003 × 108
Maximum2.05003 × 108
Range4920918
Interquartile range (IQR)3917308

Descriptive statistics

Standard deviation1763564.3
Coefficient of variation (CV)0.0087324609
Kurtosis-1.1241195
Mean2.0195502 × 108
Median Absolute Deviation (MAD)1284531
Skewness0.59597527
Sum9.8957961 × 109
Variance3.1101591 × 1012
MonotonicityIncreasing
2023-12-10T23:45:49.627884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
201366613 16
32.7%
200082082 13
26.5%
205003000 6
 
12.2%
203999452 4
 
8.2%
202000008 2
 
4.1%
200200000 1
 
2.0%
200650401 1
 
2.0%
202000000 1
 
2.0%
203999335 1
 
2.0%
203999372 1
 
2.0%
Other values (3) 3
 
6.1%
ValueCountFrequency (%)
200082082 13
26.5%
200200000 1
 
2.0%
200650401 1
 
2.0%
201366613 16
32.7%
202000000 1
 
2.0%
202000008 2
 
4.1%
203999335 1
 
2.0%
203999372 1
 
2.0%
203999390 1
 
2.0%
203999427 1
 
2.0%
ValueCountFrequency (%)
205003000 6
 
12.2%
203999452 4
 
8.2%
203999444 1
 
2.0%
203999427 1
 
2.0%
203999390 1
 
2.0%
203999372 1
 
2.0%
203999335 1
 
2.0%
202000008 2
 
4.1%
202000000 1
 
2.0%
201366613 16
32.7%

IMO_IDNTF_NO
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
36 
9420710
6 
5700730
4 
202000007
 
2
9000000
 
1

Length

Max length9
Median length1
Mean length2.6734694
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36
73.5%
9420710 6
 
12.2%
5700730 4
 
8.2%
202000007 2
 
4.1%
9000000 1
 
2.0%

Length

2023-12-10T23:45:49.760174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:45:49.860298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 36
73.5%
9420710 6
 
12.2%
5700730 4
 
8.2%
202000007 2
 
4.1%
9000000 1
 
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 00:00:41
Maximum2021-07-25 14:05:07
2023-12-10T23:45:49.968298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:50.094584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-01-01 05:05:41
Maximum2021-07-31 23:59:03
2023-12-10T23:45:50.240844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:50.377814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

DPTRP_LA
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.436631
Minimum0
Maximum48.353298
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:45:50.519911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.05996
Q121.366501
median21.455601
Q328.4902
95-th percentile48.142579
Maximum48.353298
Range48.353298
Interquartile range (IQR)7.123699

Descriptive statistics

Standard deviation10.927408
Coefficient of variation (CV)0.39827806
Kurtosis0.25720708
Mean27.436631
Median Absolute Deviation (MAD)0.26
Skewness0.79923149
Sum1344.3949
Variance119.40825
MonotonicityNot monotonic
2023-12-10T23:45:50.652167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
21.455601 1
 
2.0%
0.0 1
 
2.0%
21.1882 1
 
2.0%
20.9884 1
 
2.0%
21.2187 1
 
2.0%
21.440701 1
 
2.0%
34.880001 1
 
2.0%
47.218601 1
 
2.0%
47.219501 1
 
2.0%
47.882599 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
20.735701 1
2.0%
20.9884 1
2.0%
21.167299 1
2.0%
21.1882 1
2.0%
21.195601 1
2.0%
21.1998 1
2.0%
21.201099 1
2.0%
21.2187 1
2.0%
21.307199 1
2.0%
ValueCountFrequency (%)
48.353298 1
2.0%
48.3522 1
2.0%
48.315899 1
2.0%
47.882599 1
2.0%
47.801498 1
2.0%
47.219501 1
2.0%
47.218601 1
2.0%
45.2943 1
2.0%
44.024799 1
2.0%
43.711102 1
2.0%

DPTRP_LO
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.292568
Minimum0
Maximum128.69701
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:45:50.783539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.2521
Q150.546902
median108.462
Q3108.79
95-th percentile116.5962
Maximum128.69701
Range128.69701
Interquartile range (IQR)58.243099

Descriptive statistics

Standard deviation38.914771
Coefficient of variation (CV)0.46720581
Kurtosis-0.98512895
Mean83.292568
Median Absolute Deviation (MAD)0.554001
Skewness-0.83889708
Sum4081.3358
Variance1514.3594
MonotonicityNot monotonic
2023-12-10T23:45:50.926225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
108.314003 1
 
2.0%
0.0 1
 
2.0%
108.863998 1
 
2.0%
108.633003 1
 
2.0%
108.790001 1
 
2.0%
109.026001 1
 
2.0%
128.697006 1
 
2.0%
39.733601 1
 
2.0%
39.7355 1
 
2.0%
17.541 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
14.3185 1
2.0%
16.240499 1
2.0%
16.269501 1
2.0%
17.541 1
2.0%
17.6332 1
2.0%
24.8144 1
2.0%
26.307199 1
2.0%
27.995701 1
2.0%
39.733601 1
2.0%
ValueCountFrequency (%)
128.697006 1
2.0%
126.377998 1
2.0%
121.609001 1
2.0%
109.077003 1
2.0%
109.028 1
2.0%
109.026001 1
2.0%
109.025002 1
2.0%
109.015999 1
2.0%
108.973 1
2.0%
108.927002 1
2.0%

DTNT_LA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.144669
Minimum0
Maximum55.692402
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:45:51.065532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.2942804
Q121.3405
median21.4457
Q327.278799
95-th percentile48.0578
Maximum55.692402
Range55.692402
Interquartile range (IQR)5.938299

Descriptive statistics

Standard deviation12.080321
Coefficient of variation (CV)0.46205675
Kurtosis0.743434
Mean26.144669
Median Absolute Deviation (MAD)0.2575
Skewness0.45588261
Sum1281.0888
Variance145.93416
MonotonicityNot monotonic
2023-12-10T23:45:51.478222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.0 3
 
6.1%
21.436001 1
 
2.0%
21.5056 1
 
2.0%
21.1882 1
 
2.0%
20.9884 1
 
2.0%
21.2187 1
 
2.0%
21.440701 1
 
2.0%
35.505199 1
 
2.0%
47.219501 1
 
2.0%
55.692402 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
0.0 3
6.1%
20.735701 1
 
2.0%
20.9884 1
 
2.0%
21.167299 1
 
2.0%
21.1882 1
 
2.0%
21.195601 1
 
2.0%
21.1998 1
 
2.0%
21.201099 1
 
2.0%
21.2187 1
 
2.0%
21.307199 1
 
2.0%
ValueCountFrequency (%)
55.692402 1
2.0%
48.219398 1
2.0%
48.174 1
2.0%
47.883499 1
2.0%
47.219501 1
2.0%
45.3078 1
2.0%
45.2943 1
2.0%
44.024799 1
2.0%
43.711102 1
2.0%
35.505199 1
2.0%

DTNT_LO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.131335
Minimum0
Maximum129.435
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:45:51.609555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.886
Q149.772499
median108.462
Q3108.79
95-th percentile116.5678
Maximum129.435
Range129.435
Interquartile range (IQR)59.017502

Descriptive statistics

Standard deviation40.835803
Coefficient of variation (CV)0.50332961
Kurtosis-1.0036749
Mean81.131335
Median Absolute Deviation (MAD)0.563004
Skewness-0.79058198
Sum3975.4354
Variance1667.5628
MonotonicityNot monotonic
2023-12-10T23:45:51.734608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.0 3
 
6.1%
108.285004 1
 
2.0%
108.487999 1
 
2.0%
108.863998 1
 
2.0%
108.633003 1
 
2.0%
108.790001 1
 
2.0%
109.026001 1
 
2.0%
129.434998 1
 
2.0%
39.7355 1
 
2.0%
37.705101 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
0.0 3
6.1%
14.715 1
 
2.0%
16.415501 1
 
2.0%
17.540001 1
 
2.0%
24.8144 1
 
2.0%
26.307199 1
 
2.0%
27.995701 1
 
2.0%
27.9972 1
 
2.0%
37.705101 1
 
2.0%
39.7355 1
 
2.0%
ValueCountFrequency (%)
129.434998 1
2.0%
129.406998 1
2.0%
121.542999 1
2.0%
109.105003 1
2.0%
109.028 1
2.0%
109.026001 1
2.0%
109.025002 1
2.0%
109.015999 1
2.0%
108.973 1
2.0%
108.927002 1
2.0%

DPTR_CN_NM
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
China
30 
Croatia
4 
South Korea
 
2
Russia
 
2
Bahrain
 
2
Other values (7)
9 

Length

Max length20
Median length5
Mean length6.5510204
Min length5

Unique

Unique5 ?
Unique (%)10.2%

Sample

1st rowChina
2nd rowChina
3rd rowChina
4th rowChina
5th rowChina

Common Values

ValueCountFrequency (%)
China 30
61.2%
Croatia 4
 
8.2%
South Korea 2
 
4.1%
Russia 2
 
4.1%
Bahrain 2
 
4.1%
Saudi Arabia 2
 
4.1%
United Arab Emirates 2
 
4.1%
Ghana 1
 
2.0%
Italy 1
 
2.0%
Greece 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2023-12-10T23:45:51.850342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china 30
52.6%
croatia 4
 
7.0%
south 2
 
3.5%
korea 2
 
3.5%
russia 2
 
3.5%
bahrain 2
 
3.5%
saudi 2
 
3.5%
arabia 2
 
3.5%
united 2
 
3.5%
arab 2
 
3.5%
Other values (6) 7
 
12.3%

ARVL_CN_NM
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
China
29 
Ghana
3 
United Arab Emirates
3 
South Korea
 
2
Russia
 
2
Other values (7)
10 

Length

Max length20
Median length5
Mean length6.7755102
Min length5

Unique

Unique4 ?
Unique (%)8.2%

Sample

1st rowChina
2nd rowChina
3rd rowChina
4th rowChina
5th rowChina

Common Values

ValueCountFrequency (%)
China 29
59.2%
Ghana 3
 
6.1%
United Arab Emirates 3
 
6.1%
South Korea 2
 
4.1%
Russia 2
 
4.1%
Croatia 2
 
4.1%
Romania 2
 
4.1%
Saudi Arabia 2
 
4.1%
Italy 1
 
2.0%
Greece 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2023-12-10T23:45:51.970555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china 29
49.2%
ghana 3
 
5.1%
united 3
 
5.1%
arab 3
 
5.1%
emirates 3
 
5.1%
south 2
 
3.4%
korea 2
 
3.4%
russia 2
 
3.4%
croatia 2
 
3.4%
romania 2
 
3.4%
Other values (6) 8
 
13.6%

DPRT_PRT_CD
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
VN0088
9 
CN0604
8 
CN0009
7 
HR0001
4 
CN0030
3 
Other values (16)
18 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique14 ?
Unique (%)28.6%

Sample

1st rowCN0604
2nd rowCN0030
3rd rowCN0604
4th rowVN0088
5th rowCN0604

Common Values

ValueCountFrequency (%)
VN0088 9
18.4%
CN0604 8
16.3%
CN0009 7
14.3%
HR0001 4
 
8.2%
CN0030 3
 
6.1%
CN0155 2
 
4.1%
RU0153 2
 
4.1%
RO0009 1
 
2.0%
KR0087 1
 
2.0%
CN0577 1
 
2.0%
Other values (11) 11
22.4%

Length

2023-12-10T23:45:52.076030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vn0088 9
18.4%
cn0604 8
16.3%
cn0009 7
14.3%
hr0001 4
 
8.2%
cn0030 3
 
6.1%
cn0155 2
 
4.1%
ru0153 2
 
4.1%
gh0032 1
 
2.0%
bg0017 1
 
2.0%
ae0019 1
 
2.0%
Other values (11) 11
22.4%

DPTR_PRT_NM
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Offshore Gulf of Tonkin No. 2 (TA)
9 
Fangcheng Bulk Terminal
8 
Bei Hai
7 
Bakar
4 
Fangcheng
3 
Other values (16)
18 

Length

Max length42
Median length33
Mean length17.244898
Min length5

Unique

Unique14 ?
Unique (%)28.6%

Sample

1st rowFangcheng Bulk Terminal
2nd rowFangcheng
3rd rowFangcheng Bulk Terminal
4th rowOffshore Gulf of Tonkin No. 2 (TA)
5th rowFangcheng Bulk Terminal

Common Values

ValueCountFrequency (%)
Offshore Gulf of Tonkin No. 2 (TA) 9
18.4%
Fangcheng Bulk Terminal 8
16.3%
Bei Hai 7
14.3%
Bakar 4
 
8.2%
Fangcheng 3
 
6.1%
Weizhou 2
 
4.1%
Rostov 2
 
4.1%
Galatzi 1
 
2.0%
Mokpo Hyundai Samho Wharf 1
 
2.0%
Hongqiao Ore Terminal 1
 
2.0%
Other values (11) 11
22.4%

Length

2023-12-10T23:45:52.196308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
terminal 13
 
8.8%
offshore 11
 
7.4%
fangcheng 11
 
7.4%
gulf 9
 
6.1%
of 9
 
6.1%
tonkin 9
 
6.1%
no 9
 
6.1%
2 9
 
6.1%
ta 9
 
6.1%
bulk 8
 
5.4%
Other values (34) 51
34.5%

ARRV_PRT_CD
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
VN0088
9 
CN0604
7 
CN0009
7 
CN0030
3 
GH0032
3 
Other values (16)
20 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique12 ?
Unique (%)24.5%

Sample

1st rowCN0030
2nd rowCN0604
3rd rowVN0088
4th rowCN0604
5th rowCN0030

Common Values

ValueCountFrequency (%)
VN0088 9
18.4%
CN0604 7
14.3%
CN0009 7
14.3%
CN0030 3
 
6.1%
GH0032 3
 
6.1%
RO0009 2
 
4.1%
HR0001 2
 
4.1%
CN0155 2
 
4.1%
KR0036 2
 
4.1%
BH0008 1
 
2.0%
Other values (11) 11
22.4%

Length

2023-12-10T23:45:52.310041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vn0088 9
18.4%
cn0009 7
14.3%
cn0604 7
14.3%
cn0030 3
 
6.1%
gh0032 3
 
6.1%
ro0009 2
 
4.1%
hr0001 2
 
4.1%
cn0155 2
 
4.1%
kr0036 2
 
4.1%
bg0017 1
 
2.0%
Other values (11) 11
22.4%

ARVL_PRT_NM
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Offshore Gulf of Tonkin No. 2 (TA)
9 
Fangcheng Bulk Terminal
7 
Bei Hai
7 
Fangcheng
3 
OCTP Offshore Terminal
3 
Other values (16)
20 

Length

Max length42
Median length33
Mean length17.632653
Min length5

Unique

Unique12 ?
Unique (%)24.5%

Sample

1st rowFangcheng
2nd rowFangcheng Bulk Terminal
3rd rowOffshore Gulf of Tonkin No. 2 (TA)
4th rowFangcheng Bulk Terminal
5th rowFangcheng

Common Values

ValueCountFrequency (%)
Offshore Gulf of Tonkin No. 2 (TA) 9
18.4%
Fangcheng Bulk Terminal 7
14.3%
Bei Hai 7
14.3%
Fangcheng 3
 
6.1%
OCTP Offshore Terminal 3
 
6.1%
Galatzi 2
 
4.1%
Bakar 2
 
4.1%
Weizhou 2
 
4.1%
Ulsan 2
 
4.1%
Khalifa Bin Salman Port Container Terminal 1
 
2.0%
Other values (11) 11
22.4%

Length

2023-12-10T23:45:52.438534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
offshore 14
 
9.3%
terminal 14
 
9.3%
fangcheng 10
 
6.7%
of 9
 
6.0%
tonkin 9
 
6.0%
no 9
 
6.0%
2 9
 
6.0%
ta 9
 
6.0%
gulf 9
 
6.0%
bulk 7
 
4.7%
Other values (33) 51
34.0%

NVGTN_TM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean882.90235
Minimum0.575556
Maximum5063.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:45:52.561293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.575556
5-th percentile2.036608
Q111.4178
median97.4333
Q3455.618
95-th percentile5010.788
Maximum5063.91
Range5063.3344
Interquartile range (IQR)444.2002

Descriptive statistics

Standard deviation1668.3627
Coefficient of variation (CV)1.8896345
Kurtosis1.7765242
Mean882.90235
Median Absolute Deviation (MAD)92.57191
Skewness1.8417517
Sum43262.215
Variance2783434.1
MonotonicityNot monotonic
2023-12-10T23:45:52.704512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2.34778 1
 
2.0%
5063.91 1
 
2.0%
15.7967 1
 
2.0%
97.9839 1
 
2.0%
215.171 1
 
2.0%
0.575556 1
 
2.0%
4058.09 1
 
2.0%
455.618 1
 
2.0%
2772.43 1
 
2.0%
4994.06 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.575556 1
2.0%
0.761944 1
2.0%
1.82916 1
2.0%
2.34778 1
2.0%
2.39889 1
2.0%
2.42333 1
2.0%
4.86139 1
2.0%
6.25249 1
2.0%
7.22306 1
2.0%
7.25194 1
2.0%
ValueCountFrequency (%)
5063.91 1
2.0%
5051.62 1
2.0%
5021.94 1
2.0%
4994.06 1
2.0%
4763.22 1
2.0%
4058.09 1
2.0%
3897.62 1
2.0%
2772.43 1
2.0%
2374.77 1
2.0%
1293.13 1
2.0%

PRT_NCHRG_TM
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
0.575556
 
1

Length

Max length8
Median length3
Mean length3.1020408
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
0.575556 1
 
2.0%

Length

2023-12-10T23:45:52.831468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:45:52.915414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
0.575556 1
 
2.0%

BLLAT_HOUR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.3039
Minimum0
Maximum5063.91
Zeros37
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:45:53.010339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5010.788
Maximum5063.91
Range5063.91
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1615.0461
Coefficient of variation (CV)2.4496232
Kurtosis3.3859045
Mean659.3039
Median Absolute Deviation (MAD)0
Skewness2.2518101
Sum32305.891
Variance2608373.9
MonotonicityNot monotonic
2023-12-10T23:45:53.125837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 37
75.5%
2374.77 1
 
2.0%
97.4333 1
 
2.0%
0.761944 1
 
2.0%
4058.09 1
 
2.0%
455.618 1
 
2.0%
4994.06 1
 
2.0%
5021.94 1
 
2.0%
5051.62 1
 
2.0%
5063.91 1
 
2.0%
Other values (3) 3
 
6.1%
ValueCountFrequency (%)
0.0 37
75.5%
0.761944 1
 
2.0%
17.6558 1
 
2.0%
97.4333 1
 
2.0%
406.812 1
 
2.0%
455.618 1
 
2.0%
2374.77 1
 
2.0%
4058.09 1
 
2.0%
4763.22 1
 
2.0%
4994.06 1
 
2.0%
ValueCountFrequency (%)
5063.91 1
2.0%
5051.62 1
2.0%
5021.94 1
2.0%
4994.06 1
2.0%
4763.22 1
2.0%
4058.09 1
2.0%
2374.77 1
2.0%
455.618 1
2.0%
406.812 1
2.0%
97.4333 1
2.0%

FRGHT_CNVNC_TM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.58671
Minimum0
Maximum3897.62
Zeros13
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:45:53.237667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.6672
Q3111.851
95-th percentile966.5732
Maximum3897.62
Range3897.62
Interquartile range (IQR)111.851

Descriptive statistics

Standard deviation689.96138
Coefficient of variation (CV)3.0858784
Kurtosis20.326366
Mean223.58671
Median Absolute Deviation (MAD)11.6672
Skewness4.4354286
Sum10955.749
Variance476046.71
MonotonicityNot monotonic
2023-12-10T23:45:53.361960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 13
26.5%
2.34778 1
 
2.0%
2772.43 1
 
2.0%
468.29 1
 
2.0%
33.8803 1
 
2.0%
7.22306 1
 
2.0%
15.7967 1
 
2.0%
97.9839 1
 
2.0%
215.171 1
 
2.0%
118.052 1
 
2.0%
Other values (27) 27
55.1%
ValueCountFrequency (%)
0.0 13
26.5%
1.82916 1
 
2.0%
2.34778 1
 
2.0%
2.39889 1
 
2.0%
2.42333 1
 
2.0%
4.86139 1
 
2.0%
6.25249 1
 
2.0%
7.22306 1
 
2.0%
7.25194 1
 
2.0%
8.20111 1
 
2.0%
ValueCountFrequency (%)
3897.62 1
2.0%
2772.43 1
2.0%
1293.13 1
2.0%
476.738 1
2.0%
468.29 1
2.0%
229.666 1
2.0%
215.171 1
2.0%
207.824 1
2.0%
200.617 1
2.0%
187.834 1
2.0%

NNVGTN_TM
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
100.0%

Length

2023-12-10T23:45:53.480558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:45:53.568724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

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
2023-12-10T23:45:53.675279image/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
2023-12-10T23:45:53.811379image/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

2023-12-10T23:45:48.441898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:41.983650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.751647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.544527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.348678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.128160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.895525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:46.931825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.669513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.517755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.056575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.866992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.637850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.433831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.218247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.975520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.020524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.764187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.585395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.135702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.940474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.718093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.509291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.308231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:46.048389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.093669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.844257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.666330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.217020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.024506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.802322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.613525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.396712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:46.132293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.172939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.930151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.734777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.288376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.098279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.887263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.694802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.491928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:46.229224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.258144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.012214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.807695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.359758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.194309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.980435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.777185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.571689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:46.314649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.332105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.097378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.889907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.447244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.277774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.062296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.875161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.650366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:46.392273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.420244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.179707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.967186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.528612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.370172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.150173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.951508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.728211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:46.464540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.488893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.270816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:49.059070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:42.655200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:43.463650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:44.267891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.044774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:45.815260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:46.556525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:47.583926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:45:48.363988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:45:53.923658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MMSIIMO_IDNTF_NODPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LODPTR_CN_NMARVL_CN_NMDPRT_PRT_CDDPTR_PRT_NMARRV_PRT_CDARVL_PRT_NMNVGTN_TMPRT_NCHRG_TMBLLAT_HOURFRGHT_CNVNC_TMRN
MMSI1.0000.8241.0001.0000.8590.8330.9340.8140.9620.9541.0001.0000.9490.9490.7620.0000.8610.0000.892
IMO_IDNTF_NO0.8241.0001.0001.0000.9350.7910.8780.7890.9590.9591.0001.0000.9690.9690.7440.0000.6120.7080.943
DPTR_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ARVL_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
DPTRP_LA0.8590.9351.0001.0001.0000.9220.9070.8460.9820.9371.0001.0000.9600.9600.7250.0000.7250.0000.891
DPTRP_LO0.8330.7911.0001.0000.9221.0000.8360.9770.9830.9181.0001.0000.9880.9880.8710.0000.7360.3490.753
DTNT_LA0.9340.8781.0001.0000.9070.8361.0000.9080.9370.9720.9550.9551.0001.0000.8370.6000.9260.7020.745
DTNT_LO0.8140.7891.0001.0000.8460.9770.9081.0000.9190.9740.9850.9851.0001.0000.8470.4420.7320.1530.770
DPTR_CN_NM0.9620.9591.0001.0000.9820.9830.9370.9191.0000.9901.0001.0000.9770.9770.8730.0000.9640.6690.747
ARVL_CN_NM0.9540.9591.0001.0000.9370.9180.9720.9740.9901.0000.9770.9771.0001.0000.8600.4500.9470.6700.767
DPRT_PRT_CD1.0001.0001.0001.0001.0001.0000.9550.9851.0000.9771.0001.0000.9890.9890.9740.0001.0000.2570.799
DPTR_PRT_NM1.0001.0001.0001.0001.0001.0000.9550.9851.0000.9771.0001.0000.9890.9890.9740.0001.0000.2570.799
ARRV_PRT_CD0.9490.9691.0001.0000.9600.9881.0001.0000.9771.0000.9890.9891.0001.0000.9330.0000.8130.7560.739
ARVL_PRT_NM0.9490.9691.0001.0000.9600.9881.0001.0000.9771.0000.9890.9891.0001.0000.9330.0000.8130.7560.739
NVGTN_TM0.7620.7441.0001.0000.7250.8710.8370.8470.8730.8600.9740.9740.9330.9331.0000.0001.0000.9010.654
PRT_NCHRG_TM0.0000.0001.0001.0000.0000.0000.6000.4420.0000.4500.0000.0000.0000.0000.0001.0000.0000.0000.000
BLLAT_HOUR0.8610.6121.0001.0000.7250.7360.9260.7320.9640.9471.0001.0000.8130.8131.0000.0001.0000.0000.763
FRGHT_CNVNC_TM0.0000.7081.0001.0000.0000.3490.7020.1530.6690.6700.2570.2570.7560.7560.9010.0000.0001.0000.000
RN0.8920.9431.0001.0000.8910.7530.7450.7700.7470.7670.7990.7990.7390.7390.6540.0000.7630.0001.000
2023-12-10T23:45:54.082168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRT_NCHRG_TMDPRT_PRT_CDARVL_PRT_NMDPTR_CN_NMARRV_PRT_CDDPTR_PRT_NMARVL_CN_NMIMO_IDNTF_NO
PRT_NCHRG_TM1.0000.0000.0000.0000.0000.0000.3040.000
DPRT_PRT_CD0.0001.0000.7220.8700.7221.0000.7400.798
ARVL_PRT_NM0.0000.7221.0000.7371.0000.7220.8700.704
DPTR_CN_NM0.0000.8700.7371.0000.7370.8700.8050.837
ARRV_PRT_CD0.0000.7221.0000.7371.0000.7220.8700.704
DPTR_PRT_NM0.0001.0000.7220.8700.7221.0000.7400.798
ARVL_CN_NM0.3040.7400.8700.8050.8700.7401.0000.837
IMO_IDNTF_NO0.0000.7980.7040.8370.7040.7980.8371.000
2023-12-10T23:45:54.206284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MMSIDPTRP_LADPTRP_LODTNT_LADTNT_LONVGTN_TMBLLAT_HOURFRGHT_CNVNC_TMRNIMO_IDNTF_NODPTR_CN_NMARVL_CN_NMDPRT_PRT_CDDPTR_PRT_NMARRV_PRT_CDARVL_PRT_NMPRT_NCHRG_TM
MMSI1.0000.463-0.4950.373-0.5110.2730.326-0.1530.9720.7320.6150.5990.7520.7520.6850.6850.000
DPTRP_LA0.4631.000-0.5320.521-0.5100.2900.499-0.3680.4240.6440.8910.7870.7980.7980.6800.6800.000
DPTRP_LO-0.495-0.5321.000-0.5070.797-0.325-0.2750.097-0.4740.6510.9020.7270.8160.8160.6830.6830.000
DTNT_LA0.3730.521-0.5071.000-0.3180.3540.332-0.0920.3500.7590.7030.7990.6700.6700.8260.8260.421
DTNT_LO-0.511-0.5100.797-0.3181.000-0.217-0.2520.191-0.5010.6490.7290.8750.6690.6690.8160.8160.446
NVGTN_TM0.2730.290-0.3250.354-0.2171.0000.5210.1690.2510.5880.6360.6130.6290.6290.5290.5290.000
BLLAT_HOUR0.3260.499-0.2750.332-0.2520.5211.000-0.7220.2840.5330.6830.6430.7890.7890.4620.4620.000
FRGHT_CNVNC_TM-0.153-0.3680.097-0.0920.1910.169-0.7221.000-0.1470.3340.4080.4090.0380.0380.3850.3850.000
RN0.9720.424-0.4740.350-0.5010.2510.284-0.1471.0000.6360.4170.4400.3710.3710.2980.2980.000
IMO_IDNTF_NO0.7320.6440.6510.7590.6490.5880.5330.3340.6361.0000.8370.8370.7980.7980.7040.7040.000
DPTR_CN_NM0.6150.8910.9020.7030.7290.6360.6830.4080.4170.8371.0000.8050.8700.8700.7370.7370.000
ARVL_CN_NM0.5990.7870.7270.7990.8750.6130.6430.4090.4400.8370.8051.0000.7400.7400.8700.8700.304
DPRT_PRT_CD0.7520.7980.8160.6700.6690.6290.7890.0380.3710.7980.8700.7401.0001.0000.7220.7220.000
DPTR_PRT_NM0.7520.7980.8160.6700.6690.6290.7890.0380.3710.7980.8700.7401.0001.0000.7220.7220.000
ARRV_PRT_CD0.6850.6800.6830.8260.8160.5290.4620.3850.2980.7040.7370.8700.7220.7221.0001.0000.000
ARVL_PRT_NM0.6850.6800.6830.8260.8160.5290.4620.3850.2980.7040.7370.8700.7220.7221.0001.0000.000
PRT_NCHRG_TM0.0000.0000.0000.4210.4460.0000.0000.0000.0000.0000.0000.3040.0000.0000.0000.0001.000

Missing values

2023-12-10T23:45:49.185360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:45:49.396486image/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

MMSIIMO_IDNTF_NODPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LODPTR_CN_NMARVL_CN_NMDPRT_PRT_CDDPTR_PRT_NMARRV_PRT_CDARVL_PRT_NMNVGTN_TMPRT_NCHRG_TMBLLAT_HOURFRGHT_CNVNC_TMNNVGTN_TMRN
0200082082007-Mar-2021 18:20:0607-Mar-2021 20:40:5821.455601108.31400321.436001108.285004ChinaChinaCN0604Fangcheng Bulk TerminalCN0030Fangcheng2.347780.00.02.3477802
1200082082007-Mar-2021 20:40:5808-Mar-2021 03:56:0521.436001108.28500421.5056108.487999ChinaChinaCN0030FangchengCN0604Fangcheng Bulk Terminal7.251940.00.07.2519403
2200082082008-Mar-2021 03:56:0512-Mar-2021 10:31:0121.5056108.48799921.405899108.589996ChinaChinaCN0604Fangcheng Bulk TerminalVN0088Offshore Gulf of Tonkin No. 2 (TA)102.5820.00.0102.58204
3200082082012-Mar-2021 10:31:0102-Apr-2021 07:15:1621.405899108.58999621.430099108.344002ChinaChinaVN0088Offshore Gulf of Tonkin No. 2 (TA)CN0604Fangcheng Bulk Terminal476.7380.00.0476.73805
4200082082002-Apr-2021 07:15:1611-Apr-2021 20:55:1321.430099108.34400221.434999108.293999ChinaChinaCN0604Fangcheng Bulk TerminalCN0030Fangcheng229.6660.00.0229.66606
5200082082011-Apr-2021 20:55:1312-Apr-2021 08:35:1521.434999108.29399921.4457108.480003ChinaChinaCN0030FangchengCN0604Fangcheng Bulk Terminal11.66720.00.011.667207
6200082082012-Apr-2021 08:35:1512-Apr-2021 11:00:3921.4457108.48000321.366501108.532997ChinaChinaCN0604Fangcheng Bulk TerminalVN0088Offshore Gulf of Tonkin No. 2 (TA)2.423330.00.02.4233308
7200082082012-Apr-2021 11:00:3914-Apr-2021 08:50:1521.366501108.53299721.4669108.355003ChinaChinaVN0088Offshore Gulf of Tonkin No. 2 (TA)CN0604Fangcheng Bulk Terminal45.82670.00.045.826709
8200082082014-Apr-2021 08:50:1514-Apr-2021 15:05:2421.4669108.35500321.440599108.264ChinaChinaCN0604Fangcheng Bulk TerminalCN0030Fangcheng6.252490.00.06.25249010
9200082082014-Apr-2021 15:05:2415-Apr-2021 02:30:2821.440599108.26421.452299108.485001ChinaChinaCN0030FangchengCN0604Fangcheng Bulk Terminal11.41780.00.011.4178011
MMSIIMO_IDNTF_NODPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LODPTR_CN_NMARVL_CN_NMDPRT_PRT_CDDPTR_PRT_NMARRV_PRT_CDARVL_PRT_NMNVGTN_TMPRT_NCHRG_TMBLLAT_HOURFRGHT_CNVNC_TMNNVGTN_TMRN
39203999452570073021-Jan-2021 07:36:1708-Feb-2021 06:25:0048.31589914.318543.71110224.8144ItalyGreeceIT0101MonfalconeGR0335Nea Karvali406.8120.0406.8120.0041
40203999452570073008-Feb-2021 06:25:0013-Feb-2021 04:28:0743.71110224.814445.294327.995701GreeceRomaniaGR0335Nea KarvaliRO0009Galatzi118.0520.00.0118.052042
41203999452570073013-Feb-2021 04:28:0725-Jul-2021 14:05:0745.294327.99570144.02479926.307199RomaniaBulgariaRO0009GalatziBG0017Varna3897.620.00.03897.62043
42203999452570073025-Jul-2021 14:05:0731-Jul-2021 10:55:0644.02479926.30719945.307827.9972BulgariaRomaniaBG0017VarnaRO0009Galatzi140.8330.00.0140.833044
43205003000942071001-Jan-2021 00:00:4201-Jan-2021 17:40:0326.16290150.66799926.336850.8064BahrainBahrainBH0003BahrainBH0008Khalifa Bin Salman Port Container Terminal17.65580.017.65580.0045
44205003000942071001-Jan-2021 17:40:0302-Jan-2021 01:53:4926.336850.806427.27879950.546902BahrainSaudi ArabiaBH0008Khalifa Bin Salman Port Container TerminalSA0020Juaymah8.229440.00.08.22944046
45205003000942071002-Jan-2021 01:53:4906-Jan-2021 17:44:5227.27879950.54690227.09989949.772499Saudi ArabiaSaudi ArabiaSA0020JuaymahSA0071Jubail - Open Sea Tanker Terminal111.8510.00.0111.851047
46205003000942071006-Jan-2021 17:44:5207-Jan-2021 20:16:2727.09989949.77249925.984155.6077Saudi ArabiaUnited Arab EmiratesSA0071Jubail - Open Sea Tanker TerminalAE0019Hulaylah Terminal26.52640.00.026.5264048
47205003000942071007-Jan-2021 20:16:2707-Jan-2021 22:40:2325.984155.607726.289256.001202United Arab EmiratesUnited Arab EmiratesAE0019Hulaylah TerminalAE0085Mina Saqr Offshore RV2.398890.00.02.39889049
48205003000942071007-Jan-2021 22:40:2308-Jan-2021 06:52:2726.289256.00120225.45280156.716099United Arab EmiratesUnited Arab EmiratesAE0085Mina Saqr Offshore RVAE0035Offshore Khor Fakkan (TSA)8.201110.00.08.20111050