Overview

Dataset statistics

Number of variables10
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory91.7 B

Variable types

DateTime1
Categorical2
Numeric7

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001279

Alerts

MMSI has constant value ""Constant
IMO_IDNTF_NO has constant value ""Constant
LA is highly overall correlated with LOHigh correlation
LO is highly overall correlated with LAHigh correlation
MAX_VE is highly overall correlated with AVE_VE and 1 other fieldsHigh correlation
AVE_VE is highly overall correlated with MAX_VE and 1 other fieldsHigh correlation
NVGTN_DIST is highly overall correlated with MAX_VE and 1 other fieldsHigh correlation
YMD has unique valuesUnique
AVE_VE has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:24:36.645210
Analysis finished2023-12-10 14:24:43.238108
Duration6.59 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
Minimum2023-01-09 23:59:59
Maximum2023-04-03 23:59:59
2023-12-10T23:24:43.316619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:43.534360image/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
532694333
49 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row532694333
2nd row532694333
3rd row532694333
4th row532694333
5th row532694333

Common Values

ValueCountFrequency (%)
532694333 49
100.0%

Length

2023-12-10T23:24:43.697181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:24:43.808200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
532694333 49
100.0%

IMO_IDNTF_NO
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2769547
49 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2769547
2nd row2769547
3rd row2769547
4th row2769547
5th row2769547

Common Values

ValueCountFrequency (%)
2769547 49
100.0%

Length

2023-12-10T23:24:43.906661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:24:44.003669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2769547 49
100.0%

LA
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.618998
Minimum51.8773
Maximum56.034901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:24:44.119862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.8773
5-th percentile51.877559
Q152.211102
median53.326599
Q354.374001
95-th percentile56.0261
Maximum56.034901
Range4.157601
Interquartile range (IQR)2.162899

Descriptive statistics

Standard deviation1.5159348
Coefficient of variation (CV)0.028272344
Kurtosis-1.0326964
Mean53.618998
Median Absolute Deviation (MAD)1.115497
Skewness0.55493954
Sum2627.3309
Variance2.2980582
MonotonicityNot monotonic
2023-12-10T23:24:44.268047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
53.3265 5
 
10.2%
53.3274 3
 
6.1%
53.7388 3
 
6.1%
56.024899 3
 
6.1%
51.8773 2
 
4.1%
56.0261 2
 
4.1%
51.8778 2
 
4.1%
52.298901 1
 
2.0%
52.211102 1
 
2.0%
54.9715 1
 
2.0%
Other values (26) 26
53.1%
ValueCountFrequency (%)
51.8773 2
4.1%
51.877399 1
2.0%
51.8778 2
4.1%
51.877899 1
2.0%
51.879002 1
2.0%
51.8862 1
2.0%
51.8876 1
2.0%
51.984299 1
2.0%
52.137299 1
2.0%
52.203098 1
2.0%
ValueCountFrequency (%)
56.034901 1
 
2.0%
56.032101 1
 
2.0%
56.0261 2
4.1%
56.026001 1
 
2.0%
56.025501 1
 
2.0%
56.025398 1
 
2.0%
56.0252 1
 
2.0%
56.024899 3
6.1%
54.9715 1
 
2.0%
54.374001 1
 
2.0%

LO
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9009744
Minimum-3.69834
Maximum6.93583
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:24:44.479475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.69834
5-th percentile-3.69774
Q1-0.264432
median2.82529
Q34.42587
95-th percentile6.935788
Maximum6.93583
Range10.63417
Interquartile range (IQR)4.690302

Descriptive statistics

Standard deviation3.7971257
Coefficient of variation (CV)1.9974628
Kurtosis-1.2476383
Mean1.9009744
Median Absolute Deviation (MAD)3.085513
Skewness-0.26247899
Sum93.147745
Variance14.418164
MonotonicityNot monotonic
2023-12-10T23:24:44.697187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4.42588 2
 
4.1%
6.9358 2
 
4.1%
-0.260247 1
 
2.0%
2.82529 1
 
2.0%
3.53333 1
 
2.0%
-0.260195 1
 
2.0%
4.37444 1
 
2.0%
2.20804 1
 
2.0%
-3.69421 1
 
2.0%
-3.57175 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
-3.69834 1
2.0%
-3.69829 1
2.0%
-3.69816 1
2.0%
-3.69711 1
2.0%
-3.69639 1
2.0%
-3.69617 1
2.0%
-3.69435 1
2.0%
-3.69431 1
2.0%
-3.69421 1
2.0%
-3.68967 1
2.0%
ValueCountFrequency (%)
6.93583 1
2.0%
6.9358 2
4.1%
6.93577 1
2.0%
6.93576 1
2.0%
6.93571 1
2.0%
6.93277 1
2.0%
6.93271 1
2.0%
6.93269 1
2.0%
4.95458 1
2.0%
4.42588 2
4.1%

MAX_VE
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.720464
Minimum0.0020545
Maximum18.9658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:24:44.864272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0020545
5-th percentile0.004236804
Q19.25339
median16.3958
Q317.5801
95-th percentile18.3508
Maximum18.9658
Range18.963746
Interquartile range (IQR)8.32671

Descriptive statistics

Standard deviation7.4276338
Coefficient of variation (CV)0.58391217
Kurtosis-0.6636272
Mean12.720464
Median Absolute Deviation (MAD)1.2074
Skewness-1.1191473
Sum623.30275
Variance55.169745
MonotonicityNot monotonic
2023-12-10T23:24:45.071478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
16.7915 2
 
4.1%
15.9904 1
 
2.0%
15.7394 1
 
2.0%
0.0282313 1
 
2.0%
18.8978 1
 
2.0%
17.227 1
 
2.0%
18.146 1
 
2.0%
16.8184 1
 
2.0%
16.8968 1
 
2.0%
9.25339 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
0.0020545 1
2.0%
0.00251463 1
2.0%
0.00394614 1
2.0%
0.0046728 1
2.0%
0.00523179 1
2.0%
0.00607297 1
2.0%
0.0125194 1
2.0%
0.0282313 1
2.0%
0.029915 1
2.0%
0.0407119 1
2.0%
ValueCountFrequency (%)
18.9658 1
2.0%
18.8978 1
2.0%
18.4162 1
2.0%
18.2527 1
2.0%
18.2319 1
2.0%
18.146 1
2.0%
18.0762 1
2.0%
18.0427 1
2.0%
17.9287 1
2.0%
17.8293 1
2.0%

AVE_VE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.775755
Minimum0.00093916
Maximum16.7891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:24:45.239879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00093916
5-th percentile0.00204079
Q16.5173
median13.8552
Q315.2389
95-th percentile16.33028
Maximum16.7891
Range16.788161
Interquartile range (IQR)8.7216

Descriptive statistics

Standard deviation6.417992
Coefficient of variation (CV)0.59559558
Kurtosis-0.78229418
Mean10.775755
Median Absolute Deviation (MAD)1.8792
Skewness-1.017535
Sum528.01199
Variance41.190621
MonotonicityNot monotonic
2023-12-10T23:24:45.429470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
14.4424 1
 
2.0%
13.9237 1
 
2.0%
0.0138814 1
 
2.0%
16.7891 1
 
2.0%
13.1241 1
 
2.0%
12.8273 1
 
2.0%
16.5042 1
 
2.0%
14.0658 1
 
2.0%
15.1597 1
 
2.0%
6.5173 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.00093916 1
2.0%
0.00119046 1
2.0%
0.00184825 1
2.0%
0.0023296 1
2.0%
0.00349323 1
2.0%
0.00449823 1
2.0%
0.0056991 1
2.0%
0.0138814 1
2.0%
0.0181841 1
2.0%
0.031236 1
2.0%
ValueCountFrequency (%)
16.7891 1
2.0%
16.5042 1
2.0%
16.3876 1
2.0%
16.2443 1
2.0%
16.2086 1
2.0%
16.0308 1
2.0%
16.0284 1
2.0%
15.9712 1
2.0%
15.7594 1
2.0%
15.7344 1
2.0%

SH_DRCN
Real number (ℝ)

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206.67347
Minimum2
Maximum328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:24:45.600832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile24.4
Q1114
median239
Q3287
95-th percentile321.4
Maximum328
Range326
Interquartile range (IQR)173

Descriptive statistics

Standard deviation101.2545
Coefficient of variation (CV)0.48992502
Kurtosis-1.025573
Mean206.67347
Median Absolute Deviation (MAD)62
Skewness-0.63476078
Sum10127
Variance10252.474
MonotonicityNot monotonic
2023-12-10T23:24:45.748977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
278 6
 
12.2%
287 4
 
8.2%
238 4
 
8.2%
105 3
 
6.1%
2 3
 
6.1%
239 2
 
4.1%
288 2
 
4.1%
58 2
 
4.1%
301 2
 
4.1%
306 1
 
2.0%
Other values (20) 20
40.8%
ValueCountFrequency (%)
2 3
6.1%
58 2
4.1%
59 1
 
2.0%
60 1
 
2.0%
97 1
 
2.0%
104 1
 
2.0%
105 3
6.1%
114 1
 
2.0%
118 1
 
2.0%
119 1
 
2.0%
ValueCountFrequency (%)
328 1
2.0%
325 1
2.0%
323 1
2.0%
319 1
2.0%
307 1
2.0%
306 1
2.0%
301 2
4.1%
296 1
2.0%
289 1
2.0%
288 2
4.1%

NVGTN_DIST
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294147.77
Minimum27.6409
Maximum756371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:24:45.952353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27.6409
5-th percentile67.95554
Q18475.42
median345315
Q3407291
95-th percentile684864
Maximum756371
Range756343.36
Interquartile range (IQR)398815.58

Descriptive statistics

Standard deviation229055.27
Coefficient of variation (CV)0.77870817
Kurtosis-0.94232024
Mean294147.77
Median Absolute Deviation (MAD)180513
Skewness0.16531742
Sum14413241
Variance5.2466319 × 1010
MonotonicityNot monotonic
2023-12-10T23:24:46.234519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
546506.0 1
 
2.0%
236376.0 1
 
2.0%
178.249 1
 
2.0%
620654.0 1
 
2.0%
192400.0 1
 
2.0%
391097.0 1
 
2.0%
381514.0 1
 
2.0%
180701.0 1
 
2.0%
594094.0 1
 
2.0%
8475.42 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
27.6409 1
2.0%
33.1691 1
2.0%
65.9143 1
2.0%
71.0174 1
2.0%
97.3373 1
2.0%
136.171 1
2.0%
137.067 1
2.0%
178.249 1
2.0%
193.486 1
2.0%
285.53 1
2.0%
ValueCountFrequency (%)
756371.0 1
2.0%
734119.0 1
2.0%
716224.0 1
2.0%
637824.0 1
2.0%
620654.0 1
2.0%
594094.0 1
2.0%
557053.0 1
2.0%
546506.0 1
2.0%
541429.0 1
2.0%
500033.0 1
2.0%

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
2023-12-10T23:24:46.445431image/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
2023-12-10T23:24:46.693065image/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

2023-12-10T23:24:41.994559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:36.919682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.590031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:38.426682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:39.292317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:40.068342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:40.852666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:42.082918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.015578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.689744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:38.550678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:39.375034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:40.172092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:40.985247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:42.180902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.131917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.787355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:38.675529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:39.464007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:40.269196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:41.117326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:42.295568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.225921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.911736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:38.827170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:39.601280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:40.367279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:41.267627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:42.479246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.317599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:38.049422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:38.960249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:39.757362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:40.465775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:41.406564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:42.573154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.398803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:38.178259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:39.080201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:39.853032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:40.576920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:41.805816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:42.690347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:37.482931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:38.305625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:39.184721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:39.965454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:40.732299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:41.898922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:24:46.838400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YMDLALOMAX_VEAVE_VESH_DRCNNVGTN_DISTRN
YMD1.0001.0001.0001.0001.0001.0001.0001.000
LA1.0001.0000.9300.5880.5610.8210.7410.518
LO1.0000.9301.0000.6620.6860.6990.8000.566
MAX_VE1.0000.5880.6621.0000.9490.7230.9450.623
AVE_VE1.0000.5610.6860.9491.0000.7340.8340.483
SH_DRCN1.0000.8210.6990.7230.7341.0000.4030.592
NVGTN_DIST1.0000.7410.8000.9450.8340.4031.0000.587
RN1.0000.5180.5660.6230.4830.5920.5871.000
2023-12-10T23:24:47.004112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LALOMAX_VEAVE_VESH_DRCNNVGTN_DISTRN
LA1.000-0.688-0.435-0.445-0.445-0.1780.016
LO-0.6881.000-0.085-0.0610.277-0.255-0.491
MAX_VE-0.435-0.0851.0000.9010.2000.7500.447
AVE_VE-0.445-0.0610.9011.0000.2040.8310.338
SH_DRCN-0.4450.2770.2000.2041.0000.100-0.001
NVGTN_DIST-0.178-0.2550.7500.8310.1001.0000.339
RN0.016-0.4910.4470.338-0.0010.3391.000

Missing values

2023-12-10T23:24:42.881469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:24:43.183220image/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

YMDMMSIIMO_IDNTF_NOLALOMAX_VEAVE_VESH_DRCNNVGTN_DISTRN
009-Jan-2023 23:59:59532694333276954751.87734.4258715.990414.4424287546506.02
110-Jan-2023 23:59:59532694333276954753.32656.9358316.395814.6835278374841.03
211-Jan-2023 23:59:59532694333276954753.32746.932770.1254720.1030352285.534
319-Jan-2023 23:59:59532694333276954753.32746.932690.0025150.00119227.64095
420-Jan-2023 23:59:59532694333276954753.32746.932710.0020540.000939233.16916
521-Jan-2023 23:59:59532694333276954753.32656.935760.0407120.031236278316.4157
622-Jan-2023 23:59:59532694333276954753.32656.935710.0046730.0023327865.91438
723-Jan-2023 23:59:59532694333276954753.32656.93580.0039460.00184827871.01749
824-Jan-2023 23:59:59532694333276954753.3265996.93580.0299150.018184278193.48610
925-Jan-2023 23:59:59532694333276954753.32656.935770.0125190.005699278136.17111
YMDMMSIIMO_IDNTF_NOLALOMAX_VEAVE_VESH_DRCNNVGTN_DISTRN
3924-Mar-2023 23:59:59532694333276954754.9715-0.72769818.416214.8965323500033.041
4025-Mar-2023 23:59:59532694333276954756.024899-3.6982916.791513.494358248889.042
4126-Mar-2023 23:59:59532694333276954756.024899-3.698340.0060730.00449858137.06743
4227-Mar-2023 23:59:59532694333276954752.2989013.0519417.928716.0284114637824.044
4328-Mar-2023 23:59:59532694333276954752.2111023.3347417.153911.7893319190185.045
4429-Mar-2023 23:59:59532694333276954753.741001-0.26443216.559913.5744184345315.046
4530-Mar-2023 23:59:59532694333276954751.87734.4258817.386615.2455288385430.047
4601-Apr-2023 23:59:59532694333276954756.024899-3.6981616.307314.472259756371.048
4702-Apr-2023 23:59:59532694333276954754.2751010.07957415.715713.7226145337509.049
4803-Apr-2023 23:59:59532694333276954751.87784.4236917.377215.2137287407291.050