Overview

Dataset statistics

Number of variables16
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory145.7 B

Variable types

DateTime1
Categorical6
Numeric9

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000359

Alerts

MMSI has constant value ""Constant
IMO_IDNTF_NO has constant value ""Constant
DRAFT has constant value ""Constant
CRG_TYP has constant value ""Constant
VE is highly overall correlated with NVGTN_DIST and 1 other fieldsHigh correlation
NVGTN_DIST is highly overall correlated with VE and 1 other fieldsHigh correlation
WAVE_HGHT is highly overall correlated with RNHigh correlation
LD_RT is highly overall correlated with VE and 1 other fieldsHigh correlation
RN is highly overall correlated with WAVE_HGHTHigh correlation
YMD has unique valuesUnique
VE has unique valuesUnique
NVGTN_DIST has unique valuesUnique
WNDRC_U has unique valuesUnique
WNDRC_V has unique valuesUnique
LD_RT has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:32:19.342328
Analysis finished2023-12-10 14:32:28.264652
Duration8.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

YMD
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-05-11 06:00:00
Maximum2021-05-23 06:00:00
2023-12-10T23:32:28.335512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:28.495318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

MMSI
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
100000001
49 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100000001
2nd row100000001
3rd row100000001
4th row100000001
5th row100000001

Common Values

ValueCountFrequency (%)
100000001 49
100.0%

Length

2023-12-10T23:32:28.609920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:28.701828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100000001 49
100.0%

LA
Categorical

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
2.81495
19 
2.81497
10 
2.81496
10 
2.81494
7 
2.81493
3 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.81495
2nd row2.81495
3rd row2.81495
4th row2.81497
5th row2.81497

Common Values

ValueCountFrequency (%)
2.81495 19
38.8%
2.81497 10
20.4%
2.81496 10
20.4%
2.81494 7
 
14.3%
2.81493 3
 
6.1%

Length

2023-12-10T23:32:28.788222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:28.881875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.81495 19
38.8%
2.81497 10
20.4%
2.81496 10
20.4%
2.81494 7
 
14.3%
2.81493 3
 
6.1%

LO
Real number (ℝ)

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8311927
Minimum9.83117
Maximum9.83122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:28.972989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.83117
5-th percentile9.831174
Q19.83118
median9.83119
Q39.8312
95-th percentile9.83121
Maximum9.83122
Range5 × 10-5
Interquartile range (IQR)2 × 10-5

Descriptive statistics

Standard deviation1.3505479 × 10-5
Coefficient of variation (CV)1.3737376 × 10-6
Kurtosis-0.97048749
Mean9.8311927
Median Absolute Deviation (MAD)1 × 10-5
Skewness0.23278533
Sum481.72844
Variance1.8239796 × 10-10
MonotonicityNot monotonic
2023-12-10T23:32:29.070151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9.83118 15
30.6%
9.83119 10
20.4%
9.8312 10
20.4%
9.83121 9
18.4%
9.83117 3
 
6.1%
9.83122 2
 
4.1%
ValueCountFrequency (%)
9.83117 3
 
6.1%
9.83118 15
30.6%
9.83119 10
20.4%
9.8312 10
20.4%
9.83121 9
18.4%
9.83122 2
 
4.1%
ValueCountFrequency (%)
9.83122 2
 
4.1%
9.83121 9
18.4%
9.8312 10
20.4%
9.83119 10
20.4%
9.83118 15
30.6%
9.83117 3
 
6.1%

VE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00066443541
Minimum0.000133228
Maximum0.00183475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:29.184402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.000133228
5-th percentile0.0002110928
Q10.000405148
median0.000605419
Q30.000817927
95-th percentile0.001381964
Maximum0.00183475
Range0.001701522
Interquartile range (IQR)0.000412779

Descriptive statistics

Standard deviation0.00035838718
Coefficient of variation (CV)0.53938604
Kurtosis1.9428977
Mean0.00066443541
Median Absolute Deviation (MAD)0.000209637
Skewness1.2110846
Sum0.032557335
Variance1.2844137 × 10-7
MonotonicityNot monotonic
2023-12-10T23:32:29.305839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.000687547 1
 
2.0%
0.00034294 1
 
2.0%
0.00142442 1
 
2.0%
0.000551401 1
 
2.0%
0.00131828 1
 
2.0%
0.000706306 1
 
2.0%
0.000965468 1
 
2.0%
0.000815056 1
 
2.0%
0.000769819 1
 
2.0%
0.000746552 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.000133228 1
2.0%
0.000172799 1
2.0%
0.000196314 1
2.0%
0.000233261 1
2.0%
0.0002386 1
2.0%
0.000304898 1
2.0%
0.000305008 1
2.0%
0.00034294 1
2.0%
0.000347715 1
2.0%
0.000360648 1
2.0%
ValueCountFrequency (%)
0.00183475 1
2.0%
0.00160637 1
2.0%
0.00142442 1
2.0%
0.00131828 1
2.0%
0.00106118 1
2.0%
0.00101584 1
2.0%
0.00100279 1
2.0%
0.000965468 1
2.0%
0.000960077 1
2.0%
0.000841805 1
2.0%

SH_DRCN
Categorical

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
210
18 
211
13 
209
12 
212
6 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row210
2nd row210
3rd row212
4th row210
5th row210

Common Values

ValueCountFrequency (%)
210 18
36.7%
211 13
26.5%
209 12
24.5%
212 6
 
12.2%

Length

2023-12-10T23:32:29.427643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:29.520033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
210 18
36.7%
211 13
26.5%
209 12
24.5%
212 6
 
12.2%

IMO_IDNTF_NO
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
7924932
49 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7924932
2nd row7924932
3rd row7924932
4th row7924932
5th row7924932

Common Values

ValueCountFrequency (%)
7924932 49
100.0%

Length

2023-12-10T23:32:29.607185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:29.685311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7924932 49
100.0%

DRAFT
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

2023-12-10T23:32:29.763781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:29.836504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

CRG_TYP
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

2023-12-10T23:32:29.923172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:29.996787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

NVGTN_DIST
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3832216
Minimum1.48043
Maximum20.3878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:30.097335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.48043
5-th percentile2.34567
Q14.50201
median6.72743
Q39.08882
95-th percentile15.35634
Maximum20.3878
Range18.90737
Interquartile range (IQR)4.58681

Descriptive statistics

Standard deviation3.9824055
Coefficient of variation (CV)0.53938588
Kurtosis1.9429221
Mean7.3832216
Median Absolute Deviation (MAD)2.32949
Skewness1.211088
Sum361.77786
Variance15.859554
MonotonicityNot monotonic
2023-12-10T23:32:30.272797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
7.64005 1
 
2.0%
3.81076 1
 
2.0%
15.8281 1
 
2.0%
6.12718 1
 
2.0%
14.6487 1
 
2.0%
7.84849 1
 
2.0%
10.7283 1
 
2.0%
9.05692 1
 
2.0%
8.55425 1
 
2.0%
8.29571 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1.48043 1
2.0%
1.92015 1
2.0%
2.18145 1
2.0%
2.592 1
2.0%
2.65133 1
2.0%
3.38804 1
2.0%
3.38926 1
2.0%
3.81076 1
2.0%
3.86382 1
2.0%
4.00753 1
2.0%
ValueCountFrequency (%)
20.3878 1
2.0%
17.8501 1
2.0%
15.8281 1
2.0%
14.6487 1
2.0%
11.7919 1
2.0%
11.288 1
2.0%
11.143 1
2.0%
10.7283 1
2.0%
10.6684 1
2.0%
9.35416 1
2.0%

WAVE_HGHT
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90469388
Minimum0.77
Maximum1.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:30.401428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.77
5-th percentile0.784
Q10.84
median0.86
Q30.94
95-th percentile1.142
Maximum1.34
Range0.57
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.12008854
Coefficient of variation (CV)0.13273942
Kurtosis4.9328034
Mean0.90469388
Median Absolute Deviation (MAD)0.05
Skewness2.0429022
Sum44.33
Variance0.014421259
MonotonicityNot monotonic
2023-12-10T23:32:30.531170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.84 6
 
12.2%
0.94 4
 
8.2%
0.85 4
 
8.2%
0.86 3
 
6.1%
0.81 3
 
6.1%
0.98 2
 
4.1%
0.97 2
 
4.1%
0.9 2
 
4.1%
0.91 2
 
4.1%
0.82 2
 
4.1%
Other values (16) 19
38.8%
ValueCountFrequency (%)
0.77 1
 
2.0%
0.78 2
 
4.1%
0.79 2
 
4.1%
0.8 1
 
2.0%
0.81 3
6.1%
0.82 2
 
4.1%
0.83 1
 
2.0%
0.84 6
12.2%
0.85 4
8.2%
0.86 3
6.1%
ValueCountFrequency (%)
1.34 1
 
2.0%
1.3 1
 
2.0%
1.21 1
 
2.0%
1.04 1
 
2.0%
1.02 1
 
2.0%
1.01 1
 
2.0%
0.99 1
 
2.0%
0.98 2
4.1%
0.97 2
4.1%
0.94 4
8.2%

WAVE_CYCL
Real number (ℝ)

Distinct27
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.785057
Minimum10.6383
Maximum16.3934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:30.645457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.6383
5-th percentile10.91736
Q111.7647
median12.6582
Q313.5135
95-th percentile15.20092
Maximum16.3934
Range5.7551
Interquartile range (IQR)1.7488

Descriptive statistics

Standard deviation1.3481318
Coefficient of variation (CV)0.1054459
Kurtosis-0.029169049
Mean12.785057
Median Absolute Deviation (MAD)0.8935
Skewness0.59113663
Sum626.4678
Variance1.8174594
MonotonicityNot monotonic
2023-12-10T23:32:30.742534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12.6582 4
 
8.2%
11.7647 4
 
8.2%
13.1579 3
 
6.1%
11.4943 2
 
4.1%
12.5 2
 
4.1%
13.5135 2
 
4.1%
13.8889 2
 
4.1%
14.2857 2
 
4.1%
12.3457 2
 
4.1%
12.987 2
 
4.1%
Other values (17) 24
49.0%
ValueCountFrequency (%)
10.6383 1
 
2.0%
10.8696 2
4.1%
10.989 1
 
2.0%
11.1111 2
4.1%
11.236 1
 
2.0%
11.3636 1
 
2.0%
11.4943 2
4.1%
11.6279 1
 
2.0%
11.7647 4
8.2%
11.9048 2
4.1%
ValueCountFrequency (%)
16.3934 1
2.0%
15.625 1
2.0%
15.3846 1
2.0%
14.9254 2
4.1%
14.4928 1
2.0%
14.2857 2
4.1%
13.8889 2
4.1%
13.6986 2
4.1%
13.5135 2
4.1%
13.3333 2
4.1%

WNDRC_U
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57766326
Minimum-1.93095
Maximum4.178
Zeros0
Zeros (%)0.0%
Negative23
Negative (%)46.9%
Memory size573.0 B
2023-12-10T23:32:30.871037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.93095
5-th percentile-1.648264
Q1-0.985168
median0.0758643
Q31.90686
95-th percentile3.74726
Maximum4.178
Range6.10895
Interquartile range (IQR)2.892028

Descriptive statistics

Standard deviation1.8347401
Coefficient of variation (CV)3.1761413
Kurtosis-0.94938512
Mean0.57766326
Median Absolute Deviation (MAD)1.3741543
Skewness0.55065271
Sum28.3055
Variance3.3662714
MonotonicityNot monotonic
2023-12-10T23:32:31.011691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-1.59678 1
 
2.0%
3.49867 1
 
2.0%
0.608296 1
 
2.0%
-0.41854 1
 
2.0%
-0.152339 1
 
2.0%
-0.378909 1
 
2.0%
-1.14716 1
 
2.0%
-0.710679 1
 
2.0%
3.92734 1
 
2.0%
0.508872 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-1.93095 1
2.0%
-1.72327 1
2.0%
-1.67886 1
2.0%
-1.60237 1
2.0%
-1.59678 1
2.0%
-1.42451 1
2.0%
-1.39392 1
2.0%
-1.29829 1
2.0%
-1.22199 1
2.0%
-1.14741 1
2.0%
ValueCountFrequency (%)
4.178 1
2.0%
3.92734 1
2.0%
3.78396 1
2.0%
3.69221 1
2.0%
3.67764 1
2.0%
3.66251 1
2.0%
3.49867 1
2.0%
2.7786 1
2.0%
2.7625 1
2.0%
2.752 1
2.0%

WNDRC_V
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68273402
Minimum-2.46751
Maximum2.42696
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)26.5%
Memory size573.0 B
2023-12-10T23:32:31.127872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.46751
5-th percentile-1.186292
Q1-0.158022
median0.880107
Q31.48968
95-th percentile2.108738
Maximum2.42696
Range4.89447
Interquartile range (IQR)1.647702

Descriptive statistics

Standard deviation1.1265077
Coefficient of variation (CV)1.649995
Kurtosis0.069476856
Mean0.68273402
Median Absolute Deviation (MAD)0.720969
Skewness-0.74789796
Sum33.453967
Variance1.2690196
MonotonicityNot monotonic
2023-12-10T23:32:31.246388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1.2116 1
 
2.0%
0.75158 1
 
2.0%
0.372749 1
 
2.0%
1.95851 1
 
2.0%
-0.727422 1
 
2.0%
-1.39964 1
 
2.0%
-0.86627 1
 
2.0%
0.725771 1
 
2.0%
-0.332703 1
 
2.0%
-0.561592 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-2.46751 1
2.0%
-1.82925 1
2.0%
-1.39964 1
2.0%
-0.86627 1
2.0%
-0.804683 1
2.0%
-0.762832 1
2.0%
-0.738489 1
2.0%
-0.727422 1
2.0%
-0.561592 1
2.0%
-0.332703 1
2.0%
ValueCountFrequency (%)
2.42696 1
2.0%
2.27828 1
2.0%
2.20889 1
2.0%
1.95851 1
2.0%
1.94557 1
2.0%
1.9026 1
2.0%
1.7664 1
2.0%
1.74595 1
2.0%
1.72162 1
2.0%
1.6991 1
2.0%

LD_RT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52567916
Minimum8.56614 × 10-5
Maximum20.3342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:31.581704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.56614 × 10-5
5-th percentile0.0003833928
Q10.00677954
median0.048932
Q30.223841
95-th percentile0.4114874
Maximum20.3342
Range20.334114
Interquartile range (IQR)0.21706146

Descriptive statistics

Standard deviation2.8921137
Coefficient of variation (CV)5.5016708
Kurtosis48.757323
Mean0.52567916
Median Absolute Deviation (MAD)0.04716687
Skewness6.9746351
Sum25.758279
Variance8.3643215
MonotonicityNot monotonic
2023-12-10T23:32:31.700911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0529379 1
 
2.0%
0.00176513 1
 
2.0%
0.304827 1
 
2.0%
0.0208593 1
 
2.0%
0.431759 1
 
2.0%
0.296923 1
 
2.0%
0.36289 1
 
2.0%
0.0855645 1
 
2.0%
0.0559283 1
 
2.0%
0.168981 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
8.56614e-05 1
2.0%
0.000196884 1
2.0%
0.000296158 1
2.0%
0.000514245 1
2.0%
0.000552869 1
2.0%
0.00121304 1
2.0%
0.00176513 1
2.0%
0.00190174 1
2.0%
0.00276639 1
2.0%
0.00298626 1
2.0%
ValueCountFrequency (%)
20.3342 1
2.0%
0.44251 1
2.0%
0.431759 1
2.0%
0.38108 1
2.0%
0.36289 1
2.0%
0.359114 1
2.0%
0.345747 1
2.0%
0.328295 1
2.0%
0.319001 1
2.0%
0.304827 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
2023-12-10T23:32:31.834130image/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
2023-12-10T23:32:31.988881image/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

2023-12-10T23:32:26.842516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:19.717432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:20.451943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.424509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.153780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.893044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.586342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.319514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.985551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:26.949888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:19.797056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:20.525392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.516691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.225697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.960929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.660417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.400849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:25.076419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:27.092436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:19.899071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:20.862578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.611691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.317344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.038568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.732615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.476796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:25.213013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:27.200927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:19.998656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:20.946669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.694017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.413230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.115539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.808669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.545821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:25.426105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:27.294040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:20.071316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.026051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.771044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.501298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.187478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.890831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.623924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:25.600749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:27.405048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:20.147220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.093865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.848042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.591724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.255895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.979675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.694519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:25.775586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:27.565749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:20.231184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.166088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.921417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.673635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.325985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.068589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.762759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:25.963930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:27.698555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:20.302348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.235319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.001018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.741726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.401561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.141952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.829946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:26.569191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:27.822823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:20.382529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:21.333724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.080120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:22.825251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:23.500415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.232265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:24.912569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:26.698175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:32:32.073583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YMDLALOVESH_DRCNNVGTN_DISTWAVE_HGHTWAVE_CYCLWNDRC_UWNDRC_VLD_RTRN
YMD1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
LA1.0001.0000.2980.3010.0420.3010.0000.0000.0000.0000.1780.350
LO1.0000.2981.0000.0000.2030.0000.3280.0000.0000.3280.0000.511
VE1.0000.3010.0001.0000.2671.0000.0000.0000.0000.0001.0000.119
SH_DRCN1.0000.0420.2030.2671.0000.2670.0000.2050.3020.0000.0860.246
NVGTN_DIST1.0000.3010.0001.0000.2671.0000.0000.0000.0000.0001.0000.119
WAVE_HGHT1.0000.0000.3280.0000.0000.0001.0000.2660.3960.5550.0000.728
WAVE_CYCL1.0000.0000.0000.0000.2050.0000.2661.0000.3810.0000.3250.635
WNDRC_U1.0000.0000.0000.0000.3020.0000.3960.3811.0000.4340.0000.000
WNDRC_V1.0000.0000.3280.0000.0000.0000.5550.0000.4341.0000.0000.000
LD_RT1.0000.1780.0001.0000.0861.0000.0000.3250.0000.0001.0000.000
RN1.0000.3500.5110.1190.2460.1190.7280.6350.0000.0000.0001.000
2023-12-10T23:32:32.191211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LASH_DRCN
LA1.0000.000
SH_DRCN0.0001.000
2023-12-10T23:32:32.288444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOVENVGTN_DISTWAVE_HGHTWAVE_CYCLWNDRC_UWNDRC_VLD_RTRNLASH_DRCN
LO1.0000.3160.3160.146-0.0660.1330.0210.2210.2210.1990.120
VE0.3161.0001.0000.2280.033-0.039-0.0650.8790.1690.1040.138
NVGTN_DIST0.3161.0001.0000.2280.033-0.039-0.0650.8790.1690.1040.138
WAVE_HGHT0.1460.2280.2281.0000.165-0.0920.0140.1320.8500.0000.000
WAVE_CYCL-0.0660.0330.0330.1651.000-0.100-0.1280.0410.1310.0000.096
WNDRC_U0.133-0.039-0.039-0.092-0.1001.0000.068-0.197-0.0260.0000.160
WNDRC_V0.021-0.065-0.0650.014-0.1280.0681.000-0.131-0.0560.0000.000
LD_RT0.2210.8790.8790.1320.041-0.197-0.1311.0000.0590.2060.042
RN0.2210.1690.1690.8500.131-0.026-0.0560.0591.0000.1400.000
LA0.1990.1040.1040.0000.0000.0000.0000.2060.1401.0000.000
SH_DRCN0.1200.1380.1380.0000.0960.1600.0000.0420.0000.0001.000

Missing values

2023-12-10T23:32:27.981910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:32:28.192930image/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

YMDMMSILALOVESH_DRCNIMO_IDNTF_NODRAFTCRG_TYPNVGTN_DISTWAVE_HGHTWAVE_CYCLWNDRC_UWNDRC_VLD_RTRN
011-May-2021 06:00:001000000012.814959.831170.0006882107924932007.640050.8411.4943-1.596781.21160.0529382
111-May-2021 12:00:001000000012.814959.831190.0006292107924932006.985260.8111.2363.69221-0.1600710.0376963
211-May-2021 18:00:001000000012.814959.831180.00106121279249320011.79190.8110.9892.77861.76640.2030234
312-May-2021 00:00:001000000012.814979.831180.0001732107924932001.920150.8610.86960.017481.90260.0001975
412-May-2021 06:00:001000000012.814979.831180.0004022107924932004.472230.8210.8696-0.7666281.403070.232066
512-May-2021 12:00:001000000012.814979.831190.0001332117924932001.480430.7810.63834.178-0.3156640.0000867
612-May-2021 18:00:001000000012.814979.83120.0005232107924932005.812570.8412.65821.810962.208890.0140348
713-May-2021 00:00:001000000012.814969.831190.00160621079249320017.85010.8513.15790.6313011.489680.3591149
813-May-2021 06:00:001000000012.814969.831170.0003052097924932003.388040.8113.3333-0.8743531.443740.0225910
913-May-2021 12:00:001000000012.814969.831190.0001962107924932002.181450.7912.82052.752-1.829250.00029611
YMDMMSILALOVESH_DRCNIMO_IDNTF_NODRAFTCRG_TYPNVGTN_DISTWAVE_HGHTWAVE_CYCLWNDRC_UWNDRC_VLD_RTRN
3921-May-2021 00:00:001000000012.814959.83120.00100321079249320011.1430.9412.50.2073051.393610.08446341
4021-May-2021 06:00:001000000012.814969.831190.0009620979249320010.66840.9412.3457-0.1035550.7088010.4425142
4121-May-2021 12:00:001000000012.814979.831210.0007832107924932008.705280.9111.76473.677641.513070.04893243
4221-May-2021 18:00:001000000012.814959.83120.0008222117924932009.137450.9411.76470.7347362.426960.12475344
4322-May-2021 00:00:001000000012.814959.83120.0008282117924932009.206030.9711.62790.4472781.69910.11300745
4422-May-2021 06:00:001000000012.814969.831180.0005052097924932005.609830.9914.2857-1.147411.413340.0079146
4522-May-2021 12:00:001000000012.814939.831180.0005032097924932005.585841.0113.88891.462951.161830.00647547
4622-May-2021 18:00:001000000012.814959.831180.0003612097924932004.007531.313.51352.255411.004960.00298648
4723-May-2021 00:00:001000000012.814979.831220.0006922107924932007.685051.3412.987-1.096120.3250370.03506649
4823-May-2021 06:00:001000000012.814959.831180.0004852097924932005.386891.2112.6582-1.67886-2.467510.00859350