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

Categorical11
DateTime2
Numeric7

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001281

Alerts

MMSI has constant value ""Constant
IMO_IDNTF_NO has constant value ""Constant
BLLAT_HOUR has constant value ""Constant
FRGHT_CNVNC_TM has constant value ""Constant
NNVGTN_TM has constant value ""Constant
ARVL_PRT_NM is highly overall correlated with DTNT_LA and 5 other fieldsHigh correlation
ARVL_CN_NM is highly overall correlated with DTNT_LA and 3 other fieldsHigh correlation
DPTR_PRT_NM is highly overall correlated with DPTRP_LA and 3 other fieldsHigh correlation
ARRV_PRT_CD is highly overall correlated with DTNT_LA and 5 other fieldsHigh correlation
DPTR_CN_NM is highly overall correlated with DPTRP_LA and 3 other fieldsHigh correlation
DPRT_PRT_CD is highly overall correlated with DPTRP_LA and 3 other fieldsHigh correlation
DPTRP_LA is highly overall correlated with DPTRP_LO and 3 other fieldsHigh correlation
DPTRP_LO is highly overall correlated with DPTRP_LA and 3 other fieldsHigh correlation
DTNT_LA is highly overall correlated with DTNT_LO and 3 other fieldsHigh correlation
DTNT_LO is highly overall correlated with DTNT_LA and 3 other fieldsHigh correlation
NVGTN_TM is highly overall correlated with PRT_NCHRG_TM and 2 other fieldsHigh correlation
PRT_NCHRG_TM is highly overall correlated with NVGTN_TM and 2 other fieldsHigh correlation
DPTR_HMS has unique valuesUnique
ARVL_HMS has unique valuesUnique
NVGTN_TM has unique valuesUnique
PRT_NCHRG_TM has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:22:06.837119
Analysis finished2023-12-10 14:22:16.138140
Duration9.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MMSI
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
532613333
49 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row532613333
2nd row532613333
3rd row532613333
4th row532613333
5th row532613333

Common Values

ValueCountFrequency (%)
532613333 49
100.0%

Length

2023-12-10T23:22:16.223587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:16.320791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
532613333 49
100.0%

IMO_IDNTF_NO
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2769523
49 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2769523
2nd row2769523
3rd row2769523
4th row2769523
5th row2769523

Common Values

ValueCountFrequency (%)
2769523 49
100.0%

Length

2023-12-10T23:22:16.440641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:16.559585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2769523 49
100.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-02-28 11:55:17
Maximum2023-04-27 21:39:27
2023-12-10T23:22:16.680954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:16.840275image/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
Minimum2023-02-28 22:25:23
Maximum2023-04-29 00:02:08
2023-12-10T23:22:17.013680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:17.471995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

DPTRP_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.463478
Minimum51.876701
Maximum53.347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:22:17.688769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.876701
5-th percentile51.8783
Q151.896702
median52.263302
Q353.314999
95-th percentile53.34556
Maximum53.347
Range1.470299
Interquartile range (IQR)1.418297

Descriptive statistics

Standard deviation0.62156005
Coefficient of variation (CV)0.011847481
Kurtosis-1.4905554
Mean52.463478
Median Absolute Deviation (MAD)0.383202
Skewness0.58197459
Sum2570.7104
Variance0.3863369
MonotonicityNot monotonic
2023-12-10T23:22:17.939526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
52.263302 3
 
6.1%
51.8783 3
 
6.1%
53.345501 3
 
6.1%
52.275002 2
 
4.1%
53.3456 2
 
4.1%
53.325401 2
 
4.1%
52.241699 2
 
4.1%
53.347 1
 
2.0%
53.311901 1
 
2.0%
52.2617 1
 
2.0%
Other values (29) 29
59.2%
ValueCountFrequency (%)
51.876701 1
 
2.0%
51.8778 1
 
2.0%
51.8783 3
6.1%
51.878799 1
 
2.0%
51.8792 1
 
2.0%
51.8797 1
 
2.0%
51.8801 1
 
2.0%
51.893299 1
 
2.0%
51.8946 1
 
2.0%
51.8955 1
 
2.0%
ValueCountFrequency (%)
53.347 1
 
2.0%
53.3456 2
4.1%
53.345501 3
6.1%
53.345001 1
 
2.0%
53.341702 1
 
2.0%
53.336201 1
 
2.0%
53.330002 1
 
2.0%
53.325401 2
4.1%
53.314999 1
 
2.0%
53.311901 1
 
2.0%

DPTRP_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.3357535
Minimum-7.0344
Maximum4.42375
Zeros0
Zeros (%)0.0%
Negative30
Negative (%)61.2%
Memory size573.0 B
2023-12-10T23:22:18.121940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7.0344
5-th percentile-7.033858
Q1-6.97333
median-6.08061
Q34.32833
95-th percentile4.41899
Maximum4.42375
Range11.45815
Interquartile range (IQR)11.30166

Descriptive statistics

Standard deviation5.3486944
Coefficient of variation (CV)-2.2899225
Kurtosis-1.8396044
Mean-2.3357535
Median Absolute Deviation (MAD)0.95272
Skewness0.46372468
Sum-114.45192
Variance28.608532
MonotonicityNot monotonic
2023-12-10T23:22:18.276845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
-7.03333 3
 
6.1%
4.41833 2
 
4.1%
4.40651 1
 
2.0%
3.7 1
 
2.0%
4.32529 1
 
2.0%
-6.07898 1
 
2.0%
-6.08061 1
 
2.0%
-6.99333 1
 
2.0%
4.41667 1
 
2.0%
4.36697 1
 
2.0%
Other values (36) 36
73.5%
ValueCountFrequency (%)
-7.0344 1
 
2.0%
-7.03428 1
 
2.0%
-7.03421 1
 
2.0%
-7.03333 3
6.1%
-7.03313 1
 
2.0%
-6.99622 1
 
2.0%
-6.995 1
 
2.0%
-6.99474 1
 
2.0%
-6.99333 1
 
2.0%
-6.99167 1
 
2.0%
ValueCountFrequency (%)
4.42375 1
2.0%
4.42 1
2.0%
4.41943 1
2.0%
4.41833 2
4.1%
4.41733 1
2.0%
4.41667 1
2.0%
4.40651 1
2.0%
4.40308 1
2.0%
4.36697 1
2.0%
4.35845 1
2.0%

DTNT_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.455982
Minimum51.876701
Maximum53.347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:22:18.423443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.876701
5-th percentile51.8783
Q151.896702
median52.262001
Q353.314999
95-th percentile53.34556
Maximum53.347
Range1.470299
Interquartile range (IQR)1.418297

Descriptive statistics

Standard deviation0.62621318
Coefficient of variation (CV)0.011937879
Kurtosis-1.4953231
Mean52.455982
Median Absolute Deviation (MAD)0.381901
Skewness0.59113635
Sum2570.3431
Variance0.39214295
MonotonicityNot monotonic
2023-12-10T23:22:18.605569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
51.8783 3
 
6.1%
52.263302 3
 
6.1%
53.345501 3
 
6.1%
52.275002 2
 
4.1%
52.241699 2
 
4.1%
53.3456 2
 
4.1%
51.896702 2
 
4.1%
53.325401 2
 
4.1%
51.876701 1
 
2.0%
51.8983 1
 
2.0%
Other values (28) 28
57.1%
ValueCountFrequency (%)
51.876701 1
 
2.0%
51.8778 1
 
2.0%
51.8783 3
6.1%
51.878799 1
 
2.0%
51.8792 1
 
2.0%
51.8797 1
 
2.0%
51.8801 1
 
2.0%
51.893299 1
 
2.0%
51.8946 1
 
2.0%
51.8955 1
 
2.0%
ValueCountFrequency (%)
53.347 1
 
2.0%
53.3456 2
4.1%
53.345501 3
6.1%
53.345001 1
 
2.0%
53.341702 1
 
2.0%
53.336201 1
 
2.0%
53.330002 1
 
2.0%
53.325401 2
4.1%
53.314999 1
 
2.0%
53.311901 1
 
2.0%

DTNT_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.1035222
Minimum-7.03428
Maximum4.42375
Zeros0
Zeros (%)0.0%
Negative29
Negative (%)59.2%
Memory size573.0 B
2023-12-10T23:22:18.808189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7.03428
5-th percentile-7.03333
Q1-6.97167
median-6.07919
Q34.34493
95-th percentile4.41899
Maximum4.42375
Range11.45803
Interquartile range (IQR)11.3166

Descriptive statistics

Standard deviation5.387333
Coefficient of variation (CV)-2.5611011
Kurtosis-1.9188343
Mean-2.1035222
Median Absolute Deviation (MAD)0.95414
Skewness0.37473797
Sum-103.07259
Variance29.023357
MonotonicityNot monotonic
2023-12-10T23:22:18.970335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
-7.03333 3
 
6.1%
4.41833 2
 
4.1%
-6.995 1
 
2.0%
4.285 1
 
2.0%
4.32529 1
 
2.0%
-6.07898 1
 
2.0%
-6.08061 1
 
2.0%
-6.99333 1
 
2.0%
4.41667 1
 
2.0%
4.36697 1
 
2.0%
Other values (36) 36
73.5%
ValueCountFrequency (%)
-7.03428 1
 
2.0%
-7.03421 1
 
2.0%
-7.03333 3
6.1%
-7.03313 1
 
2.0%
-6.99622 1
 
2.0%
-6.995 1
 
2.0%
-6.99474 1
 
2.0%
-6.99333 1
 
2.0%
-6.99167 1
 
2.0%
-6.97333 1
 
2.0%
ValueCountFrequency (%)
4.42375 1
2.0%
4.42 1
2.0%
4.41943 1
2.0%
4.41833 2
4.1%
4.41733 1
2.0%
4.41667 1
2.0%
4.40651 1
2.0%
4.40308 1
2.0%
4.36697 1
2.0%
4.35845 1
2.0%

DPTR_CN_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Ireland
30 
Netherlands
19 

Length

Max length11
Median length7
Mean length8.5510204
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIreland
2nd rowIreland
3rd rowNetherlands
4th rowNetherlands
5th rowIreland

Common Values

ValueCountFrequency (%)
Ireland 30
61.2%
Netherlands 19
38.8%

Length

2023-12-10T23:22:19.167509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:19.301256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ireland 30
61.2%
netherlands 19
38.8%

ARVL_CN_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Ireland
29 
Netherlands
20 

Length

Max length11
Median length7
Mean length8.6326531
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIreland
2nd rowNetherlands
3rd rowNetherlands
4th rowIreland
5th rowIreland

Common Values

ValueCountFrequency (%)
Ireland 29
59.2%
Netherlands 20
40.8%

Length

2023-12-10T23:22:19.473211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:19.613065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ireland 29
59.2%
netherlands 20
40.8%

DPRT_PRT_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
NL0051
17 
IE0028
16 
IE0080
14 
SP5110
2 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIE0080
2nd rowIE0080
3rd rowNL0051
4th rowNL0051
5th rowIE0028

Common Values

ValueCountFrequency (%)
NL0051 17
34.7%
IE0028 16
32.7%
IE0080 14
28.6%
SP5110 2
 
4.1%

Length

2023-12-10T23:22:19.822461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:19.963878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nl0051 17
34.7%
ie0028 16
32.7%
ie0080 14
28.6%
sp5110 2
 
4.1%

DPTR_PRT_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
Rotterdam
17 
Dublin
16 
Waterford
14 
Maas Junction North Bound
2 

Length

Max length25
Median length9
Mean length8.6734694
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWaterford
2nd rowWaterford
3rd rowRotterdam
4th rowRotterdam
5th rowDublin

Common Values

ValueCountFrequency (%)
Rotterdam 17
34.7%
Dublin 16
32.7%
Waterford 14
28.6%
Maas Junction North Bound 2
 
4.1%

Length

2023-12-10T23:22:20.131788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:20.279177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rotterdam 17
30.9%
dublin 16
29.1%
waterford 14
25.5%
maas 2
 
3.6%
junction 2
 
3.6%
north 2
 
3.6%
bound 2
 
3.6%

ARRV_PRT_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
NL0051
18 
IE0028
16 
IE0080
13 
SP5110
2 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIE0080
2nd rowNL0051
3rd rowNL0051
4th rowIE0028
5th rowIE0028

Common Values

ValueCountFrequency (%)
NL0051 18
36.7%
IE0028 16
32.7%
IE0080 13
26.5%
SP5110 2
 
4.1%

Length

2023-12-10T23:22:20.421384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:20.564753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nl0051 18
36.7%
ie0028 16
32.7%
ie0080 13
26.5%
sp5110 2
 
4.1%

ARVL_PRT_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
Rotterdam
18 
Dublin
16 
Waterford
13 
Maas Junction North Bound
2 

Length

Max length25
Median length9
Mean length8.6734694
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWaterford
2nd rowRotterdam
3rd rowRotterdam
4th rowDublin
5th rowDublin

Common Values

ValueCountFrequency (%)
Rotterdam 18
36.7%
Dublin 16
32.7%
Waterford 13
26.5%
Maas Junction North Bound 2
 
4.1%

Length

2023-12-10T23:22:20.734170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:20.879692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rotterdam 18
32.7%
dublin 16
29.1%
waterford 13
23.6%
maas 2
 
3.6%
junction 2
 
3.6%
north 2
 
3.6%
bound 2
 
3.6%

NVGTN_TM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.154579
Minimum3.57139
Maximum56.4753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:22:21.022241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.57139
5-th percentile9.364556
Q114.6
median30.8689
Q341.3486
95-th percentile47.58062
Maximum56.4753
Range52.90391
Interquartile range (IQR)26.7486

Descriptive statistics

Standard deviation14.02608
Coefficient of variation (CV)0.48109354
Kurtosis-1.235449
Mean29.154579
Median Absolute Deviation (MAD)10.758
Skewness-0.19768217
Sum1428.5744
Variance196.73091
MonotonicityNot monotonic
2023-12-10T23:22:21.179789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
11.0844 1
 
2.0%
40.0772 1
 
2.0%
3.57139 1
 
2.0%
22.3289 1
 
2.0%
48.3003 1
 
2.0%
41.1269 1
 
2.0%
9.18556 1
 
2.0%
9.63305 1
 
2.0%
40.0086 1
 
2.0%
20.8 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3.57139 1
2.0%
8.52639 1
2.0%
9.18556 1
2.0%
9.63305 1
2.0%
9.72667 1
2.0%
10.1678 1
2.0%
10.6461 1
2.0%
10.9775 1
2.0%
11.0844 1
2.0%
11.5006 1
2.0%
ValueCountFrequency (%)
56.4753 1
2.0%
48.9222 1
2.0%
48.3003 1
2.0%
46.5011 1
2.0%
45.1489 1
2.0%
44.3322 1
2.0%
42.6997 1
2.0%
42.6828 1
2.0%
41.9517 1
2.0%
41.6658 1
2.0%

PRT_NCHRG_TM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.154579
Minimum3.57139
Maximum56.4753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:22:21.350981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.57139
5-th percentile9.364556
Q114.6
median30.8689
Q341.3486
95-th percentile47.58062
Maximum56.4753
Range52.90391
Interquartile range (IQR)26.7486

Descriptive statistics

Standard deviation14.02608
Coefficient of variation (CV)0.48109354
Kurtosis-1.235449
Mean29.154579
Median Absolute Deviation (MAD)10.758
Skewness-0.19768217
Sum1428.5744
Variance196.73091
MonotonicityNot monotonic
2023-12-10T23:22:21.530888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
11.0844 1
 
2.0%
40.0772 1
 
2.0%
3.57139 1
 
2.0%
22.3289 1
 
2.0%
48.3003 1
 
2.0%
41.1269 1
 
2.0%
9.18556 1
 
2.0%
9.63305 1
 
2.0%
40.0086 1
 
2.0%
20.8 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3.57139 1
2.0%
8.52639 1
2.0%
9.18556 1
2.0%
9.63305 1
2.0%
9.72667 1
2.0%
10.1678 1
2.0%
10.6461 1
2.0%
10.9775 1
2.0%
11.0844 1
2.0%
11.5006 1
2.0%
ValueCountFrequency (%)
56.4753 1
2.0%
48.9222 1
2.0%
48.3003 1
2.0%
46.5011 1
2.0%
45.1489 1
2.0%
44.3322 1
2.0%
42.6997 1
2.0%
42.6828 1
2.0%
41.9517 1
2.0%
41.6658 1
2.0%

BLLAT_HOUR
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

2023-12-10T23:22:21.718102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:21.864826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

FRGHT_CNVNC_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

2023-12-10T23:22:21.996239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:22.118189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

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

2023-12-10T23:22:22.257137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:22:22.384763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

RN
Real number (ℝ)

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
2023-12-10T23:22:22.520637image/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
2023-12-10T23:22:22.728791image/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

2023-12-10T23:22:14.787134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:09.290328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.499478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.752592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.556076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.215146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.981444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:14.904014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:09.488449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.036319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.894495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.651957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.356815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:14.113475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:15.007780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:09.666008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.203729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.006642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.745988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.462410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:14.217738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:15.147490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:09.825263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.303786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.129226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.851643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.559638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:14.355895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:15.263513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.002745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.408376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.230082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.961116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.658247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:14.496370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:15.400059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.177139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.524275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.339895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.036875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.755094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:14.597712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:15.487915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:10.345275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:11.634982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:12.458664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.123535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:13.846275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:22:14.689094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:22:22.884120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LODPTR_CN_NMARVL_CN_NMDPRT_PRT_CDDPTR_PRT_NMARRV_PRT_CDARVL_PRT_NMNVGTN_TMPRT_NCHRG_TMRN
DPTR_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ARVL_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
DPTRP_LA1.0001.0001.0001.0000.6970.6191.0000.6190.9790.9790.6820.6820.4060.4060.000
DPTRP_LO1.0001.0001.0001.0000.6480.2900.9980.2901.0001.0000.6140.6140.4660.4660.000
DTNT_LA1.0001.0000.6970.6481.0001.0000.6481.0000.6810.6810.9790.9790.9020.9020.000
DTNT_LO1.0001.0000.6190.2901.0001.0000.2900.9980.5590.5591.0001.0000.5400.5400.000
DPTR_CN_NM1.0001.0001.0000.9980.6480.2901.0000.2901.0001.0000.6140.6140.4660.4660.000
ARVL_CN_NM1.0001.0000.6190.2901.0000.9980.2901.0000.5590.5591.0001.0000.5400.5400.000
DPRT_PRT_CD1.0001.0000.9791.0000.6810.5591.0000.5591.0001.0000.7750.7750.5780.5780.258
DPTR_PRT_NM1.0001.0000.9791.0000.6810.5591.0000.5591.0001.0000.7750.7750.5780.5780.258
ARRV_PRT_CD1.0001.0000.6820.6140.9791.0000.6141.0000.7750.7751.0001.0000.7670.7670.157
ARVL_PRT_NM1.0001.0000.6820.6140.9791.0000.6141.0000.7750.7751.0001.0000.7670.7670.157
NVGTN_TM1.0001.0000.4060.4660.9020.5400.4660.5400.5780.5780.7670.7671.0001.0000.000
PRT_NCHRG_TM1.0001.0000.4060.4660.9020.5400.4660.5400.5780.5780.7670.7671.0001.0000.000
RN1.0001.0000.0000.0000.0000.0000.0000.0000.2580.2580.1570.1570.0000.0001.000
2023-12-10T23:22:23.332695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ARVL_PRT_NMARVL_CN_NMDPTR_PRT_NMARRV_PRT_CDDPTR_CN_NMDPRT_PRT_CD
ARVL_PRT_NM1.0000.9780.4111.0000.4150.411
ARVL_CN_NM0.9781.0000.3730.9780.1860.373
DPTR_PRT_NM0.4110.3731.0000.4110.9781.000
ARRV_PRT_CD1.0000.9780.4111.0000.4150.411
DPTR_CN_NM0.4150.1860.9780.4151.0000.978
DPRT_PRT_CD0.4110.3731.0000.4110.9781.000
2023-12-10T23:22:23.480125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTRP_LADPTRP_LODTNT_LADTNT_LONVGTN_TMPRT_NCHRG_TMRNDPTR_CN_NMARVL_CN_NMDPRT_PRT_CDDPTR_PRT_NMARRV_PRT_CDARVL_PRT_NM
DPTRP_LA1.000-0.6350.242-0.341-0.207-0.207-0.0470.9780.4190.8030.8030.3270.327
DPTRP_LO-0.6351.0000.0580.2170.2000.2000.1270.9560.1860.9780.9780.4150.415
DTNT_LA0.2420.0581.000-0.6700.1050.105-0.0660.4420.9780.3260.3260.8030.803
DTNT_LO-0.3410.217-0.6701.0000.4500.4500.0970.1860.9570.3730.3730.9780.978
NVGTN_TM-0.2070.2000.1050.4501.0001.000-0.0350.3210.3750.3540.3540.5380.538
PRT_NCHRG_TM-0.2070.2000.1050.4501.0001.000-0.0350.3210.3750.3540.3540.5380.538
RN-0.0470.127-0.0660.097-0.035-0.0351.0000.0000.0390.1910.1910.2180.218
DPTR_CN_NM0.9780.9560.4420.1860.3210.3210.0001.0000.1860.9780.9780.4150.415
ARVL_CN_NM0.4190.1860.9780.9570.3750.3750.0390.1861.0000.3730.3730.9780.978
DPRT_PRT_CD0.8030.9780.3260.3730.3540.3540.1910.9780.3731.0001.0000.4110.411
DPTR_PRT_NM0.8030.9780.3260.3730.3540.3540.1910.9780.3731.0001.0000.4110.411
ARRV_PRT_CD0.3270.4150.8030.9780.5380.5380.2180.4150.9780.4110.4111.0001.000
ARVL_PRT_NM0.3270.4150.8030.9780.5380.5380.2180.4150.9780.4110.4111.0001.000

Missing values

2023-12-10T23:22:15.647165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:22:16.006203image/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
0532613333276952328-Feb-2023 11:55:1728-Feb-2023 22:25:2352.264-7.034452.275002-6.995IrelandIrelandIE0080WaterfordIE0080Waterford11.084411.08440002
1532613333276952328-Feb-2023 22:25:2302-Mar-2023 17:05:1152.275002-6.99551.87784.42375IrelandNetherlandsIE0080WaterfordNL0051Rotterdam41.665841.66580003
2532613333276952302-Mar-2023 17:05:1104-Mar-2023 04:43:2651.87784.4237551.8967024.295NetherlandsNetherlandsNL0051RotterdamNL0051Rotterdam36.475636.47560004
3532613333276952304-Mar-2023 04:43:2605-Mar-2023 22:20:4651.8967024.29553.3456-6.20011NetherlandsIrelandNL0051RotterdamIE0028Dublin41.626941.62690005
4532613333276952305-Mar-2023 22:20:4607-Mar-2023 00:21:0853.3456-6.2001153.325401-6.07919IrelandIrelandIE0028DublinIE0028Dublin26.051726.05170006
5532613333276952307-Mar-2023 00:21:0807-Mar-2023 09:09:1053.325401-6.0791952.264801-7.03313IrelandIrelandIE0028DublinIE0080Waterford8.526398.526390007
6532613333276952307-Mar-2023 09:09:1007-Mar-2023 18:28:3852.264801-7.0331352.275002-6.99167IrelandIrelandIE0080WaterfordIE0080Waterford9.726679.726670008
7532613333276952307-Mar-2023 18:28:3809-Mar-2023 12:01:4552.275002-6.9916751.87834.41833IrelandNetherlandsIE0080WaterfordNL0051Rotterdam41.4341.430009
8532613333276952309-Mar-2023 12:01:4511-Mar-2023 00:18:5951.87834.4183351.8977014.35032NetherlandsNetherlandsNL0051RotterdamNL0051Rotterdam36.318136.318100010
9532613333276952311-Mar-2023 00:18:5912-Mar-2023 22:47:5851.8977014.3503253.345501-6.20022NetherlandsIrelandNL0051RotterdamIE0028Dublin46.501146.501100011
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
39532613333276952317-Apr-2023 05:11:5918-Apr-2023 20:13:3251.89834.28553.347-6.20018NetherlandsIrelandNL0051RotterdamIE0028Dublin39.106439.106400041
40532613333276952318-Apr-2023 20:13:3220-Apr-2023 03:09:0653.347-6.2001853.2183-5.98833IrelandIrelandIE0028DublinIE0028Dublin30.745630.745600042
41532613333276952320-Apr-2023 03:09:0621-Apr-2023 19:40:4453.2183-5.9883351.88014.40308IrelandNetherlandsIE0028DublinNL0051Rotterdam40.679440.679400043
42532613333276952321-Apr-2023 19:40:4422-Apr-2023 17:39:0151.88014.4030851.89554.31006NetherlandsNetherlandsNL0051RotterdamNL0051Rotterdam21.918321.918300044
43532613333276952322-Apr-2023 17:39:0124-Apr-2023 11:29:0151.89554.3100653.345501-6.20014NetherlandsIrelandNL0051RotterdamIE0028Dublin41.348641.348600045
44532613333276952324-Apr-2023 11:29:0125-Apr-2023 10:36:2153.345501-6.2001453.330002-6.085IrelandIrelandIE0028DublinIE0028Dublin23.551123.551100046
45532613333276952325-Apr-2023 10:36:2125-Apr-2023 21:57:1853.330002-6.08552.263302-7.03333IrelandIrelandIE0028DublinIE0080Waterford10.646110.646100047
46532613333276952325-Apr-2023 21:57:1826-Apr-2023 07:19:5352.263302-7.0333352.262001-6.99474IrelandIrelandIE0080WaterfordIE0080Waterford10.167810.167800048
47532613333276952326-Apr-2023 07:19:5327-Apr-2023 21:39:2752.262001-6.9947451.8767014.42IrelandNetherlandsIE0080WaterfordNL0051Rotterdam37.972237.972200049
48532613333276952327-Apr-2023 21:39:2729-Apr-2023 00:02:0851.8767014.4251.8967024.34493NetherlandsNetherlandsNL0051RotterdamNL0051Rotterdam26.611126.611100050