Overview

Dataset statistics

Number of variables20
Number of observations49
Missing cells46
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory176.7 B

Variable types

Numeric14
Text3
Categorical1
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001063

Alerts

MMSI is highly overall correlated with RNHigh correlation
IMO_IDNTF_NO is highly overall correlated with BULD_YRHigh correlation
SHIP_WDTH is highly overall correlated with SHIP_LNTH and 3 other fieldsHigh correlation
SHIP_LNTH is highly overall correlated with SHIP_WDTH and 3 other fieldsHigh correlation
SHIP_HGHT is highly overall correlated with SHIP_WDTH and 3 other fieldsHigh correlation
BULD_YR is highly overall correlated with IMO_IDNTF_NOHigh correlation
DDWGHT is highly overall correlated with SHIP_WDTH and 3 other fieldsHigh correlation
RN is highly overall correlated with MMSIHigh correlation
SHIP_KIND is highly overall correlated with SHIP_WDTH and 3 other fieldsHigh correlation
SHIP_OWNER_NM has 23 (46.9%) missing valuesMissing
SHPYRD_NM has 23 (46.9%) missing valuesMissing
MMSI has unique valuesUnique
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM has unique valuesUnique
DPTR_HMS has unique valuesUnique
ARVL_HMS has unique valuesUnique
DPTRP_LA has unique valuesUnique
DPTRP_LO has unique valuesUnique
DTNT_LA has unique valuesUnique
DTNT_LO has unique valuesUnique
FRGHT_CNVNC_QTY_TONM has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:49:55.030800
Analysis finished2023-12-10 14:50:20.507622
Duration25.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MMSI
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2930135 × 108
Minimum2.29201 × 108
Maximum2.29422 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:50:20.601266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.29201 × 108
5-th percentile2.292034 × 108
Q12.29221 × 108
median2.29285 × 108
Q32.29376 × 108
95-th percentile2.294106 × 108
Maximum2.29422 × 108
Range221000
Interquartile range (IQR)155000

Descriptive statistics

Standard deviation74115.493
Coefficient of variation (CV)0.0003232231
Kurtosis-1.4225923
Mean2.2930135 × 108
Median Absolute Deviation (MAD)70000
Skewness0.17234615
Sum1.1235766 × 1010
Variance5.4931063 × 109
MonotonicityStrictly increasing
2023-12-10T23:50:20.779242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
229201000 1
 
2.0%
229380000 1
 
2.0%
229293000 1
 
2.0%
229305000 1
 
2.0%
229308000 1
 
2.0%
229342000 1
 
2.0%
229347000 1
 
2.0%
229354000 1
 
2.0%
229361000 1
 
2.0%
229364000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
229201000 1
2.0%
229202000 1
2.0%
229203000 1
2.0%
229204000 1
2.0%
229207000 1
2.0%
229212000 1
2.0%
229213000 1
2.0%
229214000 1
2.0%
229215000 1
2.0%
229218000 1
2.0%
ValueCountFrequency (%)
229422000 1
2.0%
229421000 1
2.0%
229411000 1
2.0%
229410000 1
2.0%
229409000 1
2.0%
229397000 1
2.0%
229396000 1
2.0%
229395000 1
2.0%
229387000 1
2.0%
229382000 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9551741.1
Minimum9118678
Maximum9721683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:50:20.932125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9118678
5-th percentile9218878
Q19479204
median9609122
Q39644500
95-th percentile9662378.2
Maximum9721683
Range603005
Interquartile range (IQR)165296

Descriptive statistics

Standard deviation144194.05
Coefficient of variation (CV)0.015096101
Kurtosis1.9934674
Mean9551741.1
Median Absolute Deviation (MAD)39971
Skewness-1.5986211
Sum4.6803532 × 108
Variance2.0791923 × 1010
MonotonicityNot monotonic
2023-12-10T23:50:21.114401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9649108 1
 
2.0%
9311177 1
 
2.0%
9471630 1
 
2.0%
9630664 1
 
2.0%
9609469 1
 
2.0%
9598799 1
 
2.0%
9512331 1
 
2.0%
9457854 1
 
2.0%
9662409 1
 
2.0%
9153056 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9118678 1
2.0%
9153056 1
2.0%
9187370 1
2.0%
9266140 1
2.0%
9311177 1
2.0%
9315537 1
2.0%
9445772 1
2.0%
9455686 1
2.0%
9457854 1
2.0%
9464651 1
2.0%
ValueCountFrequency (%)
9721683 1
2.0%
9721671 1
2.0%
9662409 1
2.0%
9662332 1
2.0%
9662320 1
2.0%
9657789 1
2.0%
9657777 1
2.0%
9649108 1
2.0%
9649093 1
2.0%
9649081 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:50:21.390178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length9.1632653
Min length4

Characters and Unicode

Total characters449
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st rowDENSA PUMA
2nd rowDENSA HAWK
3rd rowDENSA FALCON
4th rowDENSA SEA LION
5th rowMayfair Spirit
ValueCountFrequency (%)
densa 4
 
5.5%
js 2
 
2.7%
nba 2
 
2.7%
lbc 2
 
2.7%
flag 2
 
2.7%
hampton 1
 
1.4%
seaeagle 1
 
1.4%
schinousa 1
 
1.4%
levante 1
 
1.4%
magritte 1
 
1.4%
Other values (56) 56
76.7%
2023-12-10T23:50:21.837945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 31
 
6.9%
a 25
 
5.6%
24
 
5.3%
i 22
 
4.9%
e 21
 
4.7%
n 21
 
4.7%
N 20
 
4.5%
S 19
 
4.2%
E 19
 
4.2%
o 18
 
4.0%
Other values (37) 229
51.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 236
52.6%
Lowercase Letter 189
42.1%
Space Separator 24
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 31
13.1%
N 20
 
8.5%
S 19
 
8.1%
E 19
 
8.1%
L 16
 
6.8%
O 16
 
6.8%
I 13
 
5.5%
D 12
 
5.1%
B 12
 
5.1%
M 12
 
5.1%
Other values (14) 66
28.0%
Lowercase Letter
ValueCountFrequency (%)
a 25
13.2%
i 22
11.6%
e 21
11.1%
n 21
11.1%
o 18
9.5%
t 11
 
5.8%
r 10
 
5.3%
s 9
 
4.8%
l 8
 
4.2%
g 7
 
3.7%
Other values (12) 37
19.6%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 425
94.7%
Common 24
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 31
 
7.3%
a 25
 
5.9%
i 22
 
5.2%
e 21
 
4.9%
n 21
 
4.9%
N 20
 
4.7%
S 19
 
4.5%
E 19
 
4.5%
o 18
 
4.2%
L 16
 
3.8%
Other values (36) 213
50.1%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 31
 
6.9%
a 25
 
5.6%
24
 
5.3%
i 22
 
4.9%
e 21
 
4.7%
n 21
 
4.7%
N 20
 
4.5%
S 19
 
4.2%
E 19
 
4.2%
o 18
 
4.0%
Other values (37) 229
51.0%

SHIP_KIND
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Bulk Carrier
24 
BULK CARRIER
23 
Chip Carrier
 
2

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBULK CARRIER
2nd rowBULK CARRIER
3rd rowBULK CARRIER
4th rowBULK CARRIER
5th rowBulk Carrier

Common Values

ValueCountFrequency (%)
Bulk Carrier 24
49.0%
BULK CARRIER 23
46.9%
Chip Carrier 2
 
4.1%

Length

2023-12-10T23:50:21.963548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:50:22.054977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
carrier 49
50.0%
bulk 47
48.0%
chip 2
 
2.0%

SHIP_WDTH
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.903673
Minimum23.5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:50:22.147797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile27.8
Q130
median32.26
Q338
95-th percentile46
Maximum50
Range26.5
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.8671836
Coefficient of variation (CV)0.19674673
Kurtosis-0.58836706
Mean34.903673
Median Absolute Deviation (MAD)3.86
Skewness0.7847713
Sum1710.28
Variance47.158211
MonotonicityNot monotonic
2023-12-10T23:50:22.282222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
32.26 13
26.5%
45.0 6
12.2%
27.8 4
 
8.2%
38.0 3
 
6.1%
30.0 3
 
6.1%
32.2 3
 
6.1%
32.0 2
 
4.1%
43.0 2
 
4.1%
36.5 2
 
4.1%
28.4 2
 
4.1%
Other values (5) 9
18.4%
ValueCountFrequency (%)
23.5 1
 
2.0%
27.8 4
 
8.2%
28.3 2
 
4.1%
28.4 2
 
4.1%
28.6 2
 
4.1%
30.0 3
 
6.1%
32.0 2
 
4.1%
32.2 3
 
6.1%
32.26 13
26.5%
36.5 2
 
4.1%
ValueCountFrequency (%)
50.0 2
 
4.1%
46.0 2
 
4.1%
45.0 6
12.2%
43.0 2
 
4.1%
38.0 3
 
6.1%
36.5 2
 
4.1%
32.26 13
26.5%
32.2 3
 
6.1%
32.0 2
 
4.1%
30.0 3
 
6.1%

SHIP_LNTH
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.56776
Minimum168.5
Maximum294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:50:22.490231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168.5
5-th percentile172
Q1181
median215
Q3225.5
95-th percentile286.74
Maximum294
Range125.5
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation40.850338
Coefficient of variation (CV)0.18862613
Kurtosis-0.79926899
Mean216.56776
Median Absolute Deviation (MAD)32
Skewness0.72475596
Sum10611.82
Variance1668.7501
MonotonicityNot monotonic
2023-12-10T23:50:22.665440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
178.0 6
 
12.2%
193.74 4
 
8.2%
217.0 4
 
8.2%
222.0 4
 
8.2%
225.5 4
 
8.2%
172.0 3
 
6.1%
282.0 3
 
6.1%
185.0 2
 
4.1%
205.0 2
 
4.1%
285.0 2
 
4.1%
Other values (13) 15
30.6%
ValueCountFrequency (%)
168.5 1
 
2.0%
171.5 1
 
2.0%
172.0 3
6.1%
176.0 1
 
2.0%
178.0 6
12.2%
181.0 1
 
2.0%
183.0 2
 
4.1%
185.0 2
 
4.1%
185.34 1
 
2.0%
193.74 4
8.2%
ValueCountFrequency (%)
294.0 2
4.1%
287.9 1
 
2.0%
285.0 2
4.1%
283.8 1
 
2.0%
282.2 1
 
2.0%
282.0 3
6.1%
248.0 1
 
2.0%
245.62 1
 
2.0%
225.5 4
8.2%
222.0 4
8.2%

SHIP_HGHT
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.115306
Minimum14.1
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:50:22.812507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.1
5-th percentile14.43
Q115.6
median18.6
Q320.7
95-th percentile24.8
Maximum24.9
Range10.8
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation3.5004232
Coefficient of variation (CV)0.18312149
Kurtosis-0.88282054
Mean19.115306
Median Absolute Deviation (MAD)2.1
Skewness0.40419716
Sum936.65
Variance12.252963
MonotonicityNot monotonic
2023-12-10T23:50:22.952365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15.6 6
12.2%
24.8 5
 
10.2%
20.05 5
 
10.2%
18.5 4
 
8.2%
14.7 3
 
6.1%
20.7 3
 
6.1%
18.0 3
 
6.1%
14.1 2
 
4.1%
17.7 2
 
4.1%
24.9 2
 
4.1%
Other values (14) 14
28.6%
ValueCountFrequency (%)
14.1 2
 
4.1%
14.25 1
 
2.0%
14.7 3
6.1%
15.2 1
 
2.0%
15.21 1
 
2.0%
15.6 6
12.2%
16.5 1
 
2.0%
17.7 2
 
4.1%
18.0 3
6.1%
18.5 4
8.2%
ValueCountFrequency (%)
24.9 2
 
4.1%
24.8 5
10.2%
24.75 1
 
2.0%
24.7 1
 
2.0%
24.5 1
 
2.0%
20.7 3
6.1%
20.2 1
 
2.0%
20.05 5
10.2%
19.9 1
 
2.0%
19.7 1
 
2.0%

SHIP_OWNER_NM
Text

MISSING 

Distinct15
Distinct (%)57.7%
Missing23
Missing (%)46.9%
Memory size524.0 B
2023-12-10T23:50:23.165083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length12.730769
Min length8

Characters and Unicode

Total characters331
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)42.3%

Sample

1st rowUnimar Success
2nd rowMinerva Marine
3rd rowGolden Union
4th rowNYK Blkshp Atlnt
5th rowNYK Blkshp Atlnt
ValueCountFrequency (%)
sea 6
 
11.3%
traders 6
 
11.3%
nyk 4
 
7.5%
blkshp 4
 
7.5%
atlnt 4
 
7.5%
dryships 3
 
5.7%
marine 3
 
5.7%
minerva 2
 
3.8%
sa 2
 
3.8%
alpha 1
 
1.9%
Other values (18) 18
34.0%
2023-12-10T23:50:23.542408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 28
 
8.5%
e 28
 
8.5%
27
 
8.2%
a 26
 
7.9%
s 24
 
7.3%
i 21
 
6.3%
n 19
 
5.7%
l 16
 
4.8%
t 12
 
3.6%
h 11
 
3.3%
Other values (28) 119
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 240
72.5%
Uppercase Letter 63
 
19.0%
Space Separator 27
 
8.2%
Other Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 28
11.7%
e 28
11.7%
a 26
10.8%
s 24
10.0%
i 21
8.8%
n 19
7.9%
l 16
 
6.7%
t 12
 
5.0%
h 11
 
4.6%
d 11
 
4.6%
Other values (9) 44
18.3%
Uppercase Letter
ValueCountFrequency (%)
S 9
14.3%
M 8
12.7%
T 7
11.1%
A 7
11.1%
B 5
7.9%
K 4
 
6.3%
Y 4
 
6.3%
N 4
 
6.3%
D 3
 
4.8%
U 2
 
3.2%
Other values (7) 10
15.9%
Space Separator
ValueCountFrequency (%)
27
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 303
91.5%
Common 28
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 28
 
9.2%
e 28
 
9.2%
a 26
 
8.6%
s 24
 
7.9%
i 21
 
6.9%
n 19
 
6.3%
l 16
 
5.3%
t 12
 
4.0%
h 11
 
3.6%
d 11
 
3.6%
Other values (26) 107
35.3%
Common
ValueCountFrequency (%)
27
96.4%
. 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 28
 
8.5%
e 28
 
8.5%
27
 
8.2%
a 26
 
7.9%
s 24
 
7.3%
i 21
 
6.3%
n 19
 
5.7%
l 16
 
4.8%
t 12
 
3.6%
h 11
 
3.3%
Other values (28) 119
36.0%

DRAFT
Real number (ℝ)

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.217122
Minimum3
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:50:23.688825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q117.5876
median28.8437
Q330
95-th percentile30
Maximum30
Range27
Interquartile range (IQR)12.4124

Descriptive statistics

Standard deviation8.8597547
Coefficient of variation (CV)0.38160434
Kurtosis0.032752595
Mean23.217122
Median Absolute Deviation (MAD)1.1563
Skewness-1.1461126
Sum1137.639
Variance78.495253
MonotonicityNot monotonic
2023-12-10T23:50:23.815798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
30.0 20
40.8%
5.0 4
 
8.2%
27.2114 4
 
8.2%
28.8437 1
 
2.0%
17.49 1
 
2.0%
17.5876 1
 
2.0%
9.0 1
 
2.0%
17.585 1
 
2.0%
17.6239 1
 
2.0%
4.0 1
 
2.0%
Other values (14) 14
28.6%
ValueCountFrequency (%)
3.0 1
 
2.0%
4.0 1
 
2.0%
5.0 4
8.2%
9.0 1
 
2.0%
17.49 1
 
2.0%
17.5141 1
 
2.0%
17.5187 1
 
2.0%
17.5503 1
 
2.0%
17.585 1
 
2.0%
17.5876 1
 
2.0%
ValueCountFrequency (%)
30.0 20
40.8%
29.0691 1
 
2.0%
28.8774 1
 
2.0%
28.8672 1
 
2.0%
28.8625 1
 
2.0%
28.8437 1
 
2.0%
27.2114 4
 
8.2%
25.2942 1
 
2.0%
24.3378 1
 
2.0%
21.8201 1
 
2.0%

SHPYRD_NM
Text

MISSING 

Distinct13
Distinct (%)50.0%
Missing23
Missing (%)46.9%
Memory size524.0 B
2023-12-10T23:50:23.992572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length15.615385
Min length12

Characters and Unicode

Total characters406
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)26.9%

Sample

1st rowJiangsu New YZJ
2nd rowShanghai Waigaoqiao
3rd rowSCS Shipbuilding
4th rowOshima Shipbuilding
5th rowOshima Shipbuilding
ValueCountFrequency (%)
new 9
14.1%
sb 8
12.5%
shipbuilding 7
 
10.9%
century 5
 
7.8%
scs 4
 
6.2%
jiangsu 3
 
4.7%
yzj 3
 
4.7%
oshima 3
 
4.7%
beihai 2
 
3.1%
shipyard 2
 
3.1%
Other values (16) 18
28.1%
2023-12-10T23:50:24.330257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 44
 
10.8%
38
 
9.4%
S 30
 
7.4%
n 26
 
6.4%
e 24
 
5.9%
u 22
 
5.4%
a 20
 
4.9%
h 20
 
4.9%
g 17
 
4.2%
s 15
 
3.7%
Other values (29) 150
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 272
67.0%
Uppercase Letter 92
 
22.7%
Space Separator 38
 
9.4%
Open Punctuation 2
 
0.5%
Close Punctuation 2
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 44
16.2%
n 26
 
9.6%
e 24
 
8.8%
u 22
 
8.1%
a 20
 
7.4%
h 20
 
7.4%
g 17
 
6.2%
s 15
 
5.5%
d 10
 
3.7%
r 9
 
3.3%
Other values (11) 65
23.9%
Uppercase Letter
ValueCountFrequency (%)
S 30
32.6%
B 11
 
12.0%
C 11
 
12.0%
N 9
 
9.8%
Z 6
 
6.5%
J 6
 
6.5%
T 5
 
5.4%
Y 4
 
4.3%
O 3
 
3.3%
P 2
 
2.2%
Other values (5) 5
 
5.4%
Space Separator
ValueCountFrequency (%)
38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 364
89.7%
Common 42
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 44
 
12.1%
S 30
 
8.2%
n 26
 
7.1%
e 24
 
6.6%
u 22
 
6.0%
a 20
 
5.5%
h 20
 
5.5%
g 17
 
4.7%
s 15
 
4.1%
B 11
 
3.0%
Other values (26) 135
37.1%
Common
ValueCountFrequency (%)
38
90.5%
( 2
 
4.8%
) 2
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 44
 
10.8%
38
 
9.4%
S 30
 
7.4%
n 26
 
6.4%
e 24
 
5.9%
u 22
 
5.4%
a 20
 
4.9%
h 20
 
4.9%
g 17
 
4.2%
s 15
 
3.7%
Other values (29) 150
36.9%

BULD_YR
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.2041
Minimum1997
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:50:24.457436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2000
Q12011
median2013
Q32013
95-th percentile2013.6
Maximum2015
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.1128842
Coefficient of variation (CV)0.002044986
Kurtosis5.7648237
Mean2011.2041
Median Absolute Deviation (MAD)0
Skewness-2.5142245
Sum98549
Variance16.915816
MonotonicityNot monotonic
2023-12-10T23:50:24.591407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2013 28
57.1%
2012 5
 
10.2%
2011 4
 
8.2%
1998 2
 
4.1%
2010 2
 
4.1%
2015 2
 
4.1%
2009 1
 
2.0%
2005 1
 
2.0%
2014 1
 
2.0%
2007 1
 
2.0%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
1997 1
 
2.0%
1998 2
 
4.1%
2003 1
 
2.0%
2005 1
 
2.0%
2007 1
 
2.0%
2009 1
 
2.0%
2010 2
 
4.1%
2011 4
 
8.2%
2012 5
 
10.2%
2013 28
57.1%
ValueCountFrequency (%)
2015 2
 
4.1%
2014 1
 
2.0%
2013 28
57.1%
2012 5
 
10.2%
2011 4
 
8.2%
2010 2
 
4.1%
2009 1
 
2.0%
2007 1
 
2.0%
2005 1
 
2.0%
2003 1
 
2.0%

DDWGHT
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86431.653
Minimum27780
Maximum206046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:50:24.748823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27780
5-th percentile33486.6
Q138980
median71663
Q393283
95-th percentile181928.2
Maximum206046
Range178266
Interquartile range (IQR)54303

Descriptive statistics

Standard deviation54355.963
Coefficient of variation (CV)0.62888955
Kurtosis-0.246337
Mean86431.653
Median Absolute Deviation (MAD)32683
Skewness1.036647
Sum4235151
Variance2.9545707 × 109
MonotonicityNot monotonic
2023-12-10T23:50:24.894853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
63200 4
 
8.2%
58000 2
 
4.1%
36722 1
 
2.0%
75884 1
 
2.0%
70663 1
 
2.0%
76036 1
 
2.0%
82099 1
 
2.0%
93207 1
 
2.0%
176247 1
 
2.0%
71663 1
 
2.0%
Other values (35) 35
71.4%
ValueCountFrequency (%)
27780 1
2.0%
32203 1
2.0%
32385 1
2.0%
35139 1
2.0%
35157 1
2.0%
35173 1
2.0%
36722 1
2.0%
36746 1
2.0%
36752 1
2.0%
36765 1
2.0%
ValueCountFrequency (%)
206046 1
2.0%
206037 1
2.0%
182307 1
2.0%
181360 1
2.0%
179667 1
2.0%
179549 1
2.0%
177933 1
2.0%
176460 1
2.0%
176247 1
2.0%
175125 1
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:49
Maximum2022-01-22 22:52:57
2023-12-10T23:50:25.058913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:25.211989image/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
Minimum2022-06-05 13:59:12
Maximum2022-07-17 22:00:08
2023-12-10T23:50:25.367321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:25.529134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1464812
Minimum-38.784401
Maximum56.5882
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)38.8%
Memory size573.0 B
2023-12-10T23:50:26.083749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-38.784401
5-th percentile-34.49122
Q1-10.0565
median10.1567
Q329.893801
95-th percentile49.37294
Maximum56.5882
Range95.372601
Interquartile range (IQR)39.950301

Descriptive statistics

Standard deviation27.953497
Coefficient of variation (CV)3.4313584
Kurtosis-1.1070732
Mean8.1464812
Median Absolute Deviation (MAD)19.8965
Skewness-0.12854323
Sum399.17758
Variance781.39799
MonotonicityNot monotonic
2023-12-10T23:50:26.231769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-33.0098 1
 
2.0%
46.292702 1
 
2.0%
29.8622 1
 
2.0%
-36.190601 1
 
2.0%
-7.18513 1
 
2.0%
29.7068 1
 
2.0%
36.248299 1
 
2.0%
-3.7475 1
 
2.0%
-9.4755 1
 
2.0%
11.9299 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-38.784401 1
2.0%
-36.190601 1
2.0%
-34.741501 1
2.0%
-34.115799 1
2.0%
-33.964401 1
2.0%
-33.926701 1
2.0%
-33.0098 1
2.0%
-30.094801 1
2.0%
-28.9121 1
2.0%
-20.385799 1
2.0%
ValueCountFrequency (%)
56.5882 1
2.0%
52.202499 1
2.0%
50.9963 1
2.0%
46.937901 1
2.0%
46.292702 1
2.0%
45.6371 1
2.0%
43.891701 1
2.0%
36.248299 1
2.0%
33.356201 1
2.0%
31.6064 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.611944
Minimum-150.99699
Maximum153.49699
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)34.7%
Memory size573.0 B
2023-12-10T23:50:26.372974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-150.99699
5-th percentile-114.09832
Q1-62.301102
median32.020599
Q3104.36
95-th percentile121.8744
Maximum153.49699
Range304.49399
Interquartile range (IQR)166.6611

Descriptive statistics

Standard deviation84.160198
Coefficient of variation (CV)4.2912725
Kurtosis-1.1376613
Mean19.611944
Median Absolute Deviation (MAD)81.638398
Skewness-0.28964412
Sum960.98527
Variance7082.9389
MonotonicityNot monotonic
2023-12-10T23:50:26.505491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-71.5923 1
 
2.0%
-124.991997 1
 
2.0%
49.200199 1
 
2.0%
52.959599 1
 
2.0%
112.685997 1
 
2.0%
-89.983498 1
 
2.0%
-2.415 1
 
2.0%
114.434998 1
 
2.0%
115.720001 1
 
2.0%
45.2719 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-150.996994 1
2.0%
-129.341995 1
2.0%
-124.991997 1
2.0%
-97.757797 1
2.0%
-90.506302 1
2.0%
-89.983498 1
2.0%
-81.235603 1
2.0%
-79.428703 1
2.0%
-78.187698 1
2.0%
-73.481201 1
2.0%
ValueCountFrequency (%)
153.496994 1
2.0%
135.139999 1
2.0%
122.036003 1
2.0%
121.632004 1
2.0%
118.633003 1
2.0%
116.57 1
2.0%
115.777 1
2.0%
115.720001 1
2.0%
114.434998 1
2.0%
113.658997 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8782175
Minimum-35.942799
Maximum59.7024
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:50:26.646414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-35.942799
5-th percentile-34.042319
Q1-20.0308
median12.5202
Q332.810001
95-th percentile47.498421
Maximum59.7024
Range95.645199
Interquartile range (IQR)52.840801

Descriptive statistics

Standard deviation28.121843
Coefficient of variation (CV)3.16751
Kurtosis-1.2240213
Mean8.8782175
Median Absolute Deviation (MAD)24.1953
Skewness-0.10236321
Sum435.03266
Variance790.83803
MonotonicityNot monotonic
2023-12-10T23:50:26.805552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
28.087601 1
 
2.0%
-23.976101 1
 
2.0%
12.5202 1
 
2.0%
-5.995 1
 
2.0%
5.9983 1
 
2.0%
25.2253 1
 
2.0%
-11.6751 1
 
2.0%
-35.049702 1
 
2.0%
41.5284 1
 
2.0%
19.0788 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-35.942799 1
2.0%
-35.049702 1
2.0%
-34.310799 1
2.0%
-33.639599 1
2.0%
-32.696201 1
2.0%
-28.962299 1
2.0%
-25.639999 1
2.0%
-24.3557 1
2.0%
-23.976101 1
2.0%
-23.846701 1
2.0%
ValueCountFrequency (%)
59.7024 1
2.0%
57.3703 1
2.0%
48.4459 1
2.0%
46.077202 1
2.0%
45.0285 1
2.0%
41.5284 1
2.0%
38.6982 1
2.0%
36.9907 1
2.0%
36.818298 1
2.0%
36.681301 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.119345
Minimum-152.45799
Maximum151.46001
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:50:26.974925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-152.45799
5-th percentile-90.024939
Q1-39.646702
median31.9053
Q3108.958
95-th percentile135.003
Maximum151.46001
Range303.918
Interquartile range (IQR)148.6047

Descriptive statistics

Standard deviation79.166141
Coefficient of variation (CV)2.8153622
Kurtosis-1.004921
Mean28.119345
Median Absolute Deviation (MAD)77.0527
Skewness-0.20502558
Sum1377.8479
Variance6267.2778
MonotonicityNot monotonic
2023-12-10T23:50:27.191392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-86.834602 1
 
2.0%
-46.287102 1
 
2.0%
47.181099 1
 
2.0%
79.550003 1
 
2.0%
94.438904 1
 
2.0%
60.595699 1
 
2.0%
-35.895302 1
 
2.0%
-56.041 1
 
2.0%
31.9053 1
 
2.0%
61.646 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-152.457993 1
2.0%
-91.505203 1
2.0%
-90.583298 1
2.0%
-89.187401 1
2.0%
-86.834602 1
2.0%
-72.620399 1
2.0%
-71.685501 1
2.0%
-60.7229 1
2.0%
-56.041 1
2.0%
-51.970299 1
2.0%
ValueCountFrequency (%)
151.460007 1
2.0%
149.348007 1
2.0%
139.852997 1
2.0%
127.727997 1
2.0%
126.724998 1
2.0%
120.25 1
2.0%
118.538002 1
2.0%
118.529999 1
2.0%
117.078003 1
2.0%
116.224998 1
2.0%

FRGHT_CNVNC_QTY_TONM
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2921849.4
Minimum323547
Maximum8786660
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:50:27.358637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum323547
5-th percentile946549.8
Q12031460
median2613150
Q33629840
95-th percentile5575868
Maximum8786660
Range8463113
Interquartile range (IQR)1598380

Descriptive statistics

Standard deviation1623869.7
Coefficient of variation (CV)0.55576776
Kurtosis2.5810383
Mean2921849.4
Median Absolute Deviation (MAD)715190
Skewness1.2802374
Sum1.4317062 × 108
Variance2.6369529 × 1012
MonotonicityNot monotonic
2023-12-10T23:50:27.575137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1582500 1
 
2.0%
2661220 1
 
2.0%
5086450 1
 
2.0%
1095210 1
 
2.0%
1751110 1
 
2.0%
2779280 1
 
2.0%
4238480 1
 
2.0%
878063 1
 
2.0%
8786660 1
 
2.0%
3328340 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
323547 1
2.0%
688133 1
2.0%
878063 1
2.0%
1049280 1
2.0%
1050850 1
2.0%
1095210 1
2.0%
1106460 1
2.0%
1389130 1
2.0%
1582500 1
2.0%
1751110 1
2.0%
ValueCountFrequency (%)
8786660 1
2.0%
6710460 1
2.0%
5728780 1
2.0%
5346500 1
2.0%
5086450 1
2.0%
5046810 1
2.0%
4793520 1
2.0%
4354150 1
2.0%
4238480 1
2.0%
4156980 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
2023-12-10T23:50:27.752650image/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
2023-12-10T23:50:27.942433image/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

2023-12-10T23:50:17.851386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:56.144212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:57.927509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.209094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:00.461584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:03.076286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:04.611424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:06.474460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:08.118364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.656803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:11.256629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:13.045162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.504014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:16.317029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:17.940663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:56.240376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.024201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.282297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:00.564369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:03.175703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:04.708158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:06.574709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:08.234422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.752337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:11.351442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:13.147749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.594701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:16.457286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:18.056674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:56.344565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.114344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.370410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:00.806271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:03.298282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:05.187577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:06.669614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:08.342669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.870371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:11.464062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:13.269422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.710376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:16.577605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:18.171637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:56.453192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.194544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.456678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:01.064096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:03.408147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:05.292193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:06.765918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:08.458585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.974237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:11.557592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:13.381962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.814556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:16.676504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:18.283757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:56.566911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.285932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.533037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:01.516150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:03.517622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:05.406809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:06.874757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:08.577978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:10.104650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:11.650290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:13.496304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.907058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:16.787119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:18.420201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:56.663830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.376836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.612859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:01.948644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:03.634307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:05.516431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:07.023197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:08.691393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:10.216183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:12.118546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:13.583094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.998099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:16.898151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:18.558844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:56.753901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.518708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.703855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:02.119023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:03.743545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:05.615367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:07.154635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:08.791427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:10.342784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:12.226029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:13.693757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:15.098566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:17.001809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:18.683798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:56.871973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.619708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.790245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:02.261149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:03.871384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:05.718152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:07.273252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:08.904982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:10.467582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:12.328578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:13.810101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:15.207313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:17.110161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:18.792190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:56.995159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.709088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.873770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:02.360172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:03.988935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:05.837452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:07.388398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.028032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:10.595366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:12.430879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:13.922238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:15.308331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:17.218444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:18.883029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:57.084669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.785449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.956460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:02.457079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:04.097218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:05.944862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:07.510527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.132667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:10.720032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:12.531033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.033061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:15.390084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:17.315822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:19.368229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:57.215374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.877824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:00.047864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:02.581510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:04.194605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:06.063185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:07.648401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.243453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:10.833055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:12.648957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.144314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:15.505532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:17.431574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:19.491421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:57.672552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:58.959541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:00.137767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:02.720081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:04.291605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:06.165674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:07.762187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.341658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:10.939028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:12.758778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.246377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:15.729730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:17.539485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:19.637429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:57.768234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.044472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:00.239732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:02.851162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:04.396127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:06.279544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:07.887729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.455172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:11.056870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:12.866055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.339522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:16.000337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:17.649754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:19.761793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:57.850106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:59.122273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:00.332109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:02.967386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:04.498829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:06.366018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:08.012596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:09.545913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:11.156930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:12.953747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:14.424033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:16.157932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:17.745451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:50:28.097237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOFRGHT_CNVNC_QTY_TONMRN
MMSI1.0000.8041.0000.6380.6200.4900.3110.8790.5260.7470.5370.2861.0001.0000.0000.0000.0000.0000.3780.910
IMO_IDNTF_NO0.8041.0001.0000.0000.7200.5230.7340.7010.4430.9120.9000.4721.0001.0000.6700.0000.1520.0000.0000.621
SHIP_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
SHIP_KIND0.6380.0001.0001.0000.9900.7900.8010.0000.5440.6100.0000.7711.0001.0000.3400.4640.3230.2530.7050.382
SHIP_WDTH0.6200.7201.0000.9901.0000.8520.8790.6090.7880.8150.6150.9371.0001.0000.4130.0000.0000.4840.6840.539
SHIP_LNTH0.4900.5231.0000.7900.8521.0000.8360.6530.8860.8080.4850.9811.0001.0000.1770.4350.0000.3540.3780.618
SHIP_HGHT0.3110.7341.0000.8010.8790.8361.0000.9290.7200.9140.7340.8161.0001.0000.0000.2930.2010.6180.4040.593
SHIP_OWNER_NM0.8790.7011.0000.0000.6090.6530.9291.0000.8710.9350.8870.0001.0001.0000.0000.7810.7520.0000.3180.907
DRAFT0.5260.4431.0000.5440.7880.8860.7200.8711.0001.0000.5760.6941.0001.0000.3520.4370.0000.1750.3550.581
SHPYRD_NM0.7470.9121.0000.6100.8150.8080.9140.9351.0001.0000.8770.5721.0001.0000.0000.7500.7040.1700.0000.762
BULD_YR0.5370.9001.0000.0000.6150.4850.7340.8870.5760.8771.0000.4241.0001.0000.4690.0000.0000.1430.3150.534
DDWGHT0.2860.4721.0000.7710.9370.9810.8160.0000.6940.5720.4241.0001.0001.0000.1660.4410.0000.3710.5890.521
DPTR_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ARVL_HMS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
DPTRP_LA0.0000.6701.0000.3400.4130.1770.0000.0000.3520.0000.4690.1661.0001.0001.0000.4250.3590.0000.2050.226
DPTRP_LO0.0000.0001.0000.4640.0000.4350.2930.7810.4370.7500.0000.4411.0001.0000.4251.0000.2940.1930.0000.243
DTNT_LA0.0000.1521.0000.3230.0000.0000.2010.7520.0000.7040.0000.0001.0001.0000.3590.2941.0000.6540.3060.000
DTNT_LO0.0000.0001.0000.2530.4840.3540.6180.0000.1750.1700.1430.3711.0001.0000.0000.1930.6541.0000.2850.442
FRGHT_CNVNC_QTY_TONM0.3780.0001.0000.7050.6840.3780.4040.3180.3550.0000.3150.5891.0001.0000.2050.0000.3060.2851.0000.320
RN0.9100.6211.0000.3820.5390.6180.5930.9070.5810.7620.5340.5211.0001.0000.2260.2430.0000.4420.3201.000
2023-12-10T23:50:28.354815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MMSIIMO_IDNTF_NOSHIP_WDTHSHIP_LNTHSHIP_HGHTDRAFTBULD_YRDDWGHTDPTRP_LADPTRP_LODTNT_LADTNT_LOFRGHT_CNVNC_QTY_TONMRNSHIP_KIND
MMSI1.000-0.2000.3660.3490.3240.049-0.1280.369-0.0590.046-0.4920.1400.1361.0000.343
IMO_IDNTF_NO-0.2001.000-0.112-0.115-0.110-0.1820.817-0.136-0.228-0.0760.195-0.0530.043-0.2000.000
SHIP_WDTH0.366-0.1121.0000.9040.900-0.046-0.0130.929-0.1910.362-0.1860.4440.4940.3660.818
SHIP_LNTH0.349-0.1150.9041.0000.9660.0780.0270.986-0.1720.317-0.2180.4420.4820.3490.707
SHIP_HGHT0.324-0.1100.9000.9661.0000.0600.0400.976-0.1790.286-0.2120.3940.4580.3240.684
DRAFT0.049-0.182-0.0460.0780.0601.000-0.2400.0860.173-0.169-0.292-0.015-0.1350.0490.410
BULD_YR-0.1280.817-0.0130.0270.040-0.2401.000-0.004-0.350-0.0410.0980.0150.102-0.1280.000
DDWGHT0.369-0.1360.9290.9860.9760.086-0.0041.000-0.1720.320-0.2190.4220.4940.3690.679
DPTRP_LA-0.059-0.228-0.191-0.172-0.1790.173-0.350-0.1721.000-0.246-0.070-0.1630.053-0.0590.136
DPTRP_LO0.046-0.0760.3620.3170.286-0.169-0.0410.320-0.2461.0000.0330.1140.0860.0460.302
DTNT_LA-0.4920.195-0.186-0.218-0.212-0.2920.098-0.219-0.0700.0331.000-0.0080.088-0.4920.178
DTNT_LO0.140-0.0530.4440.4420.394-0.0150.0150.422-0.1630.114-0.0081.0000.2870.1400.109
FRGHT_CNVNC_QTY_TONM0.1360.0430.4940.4820.458-0.1350.1020.4940.0530.0860.0880.2871.0000.1360.358
RN1.000-0.2000.3660.3490.3240.049-0.1280.369-0.0590.046-0.4920.1400.1361.0000.200
SHIP_KIND0.3430.0000.8180.7070.6840.4100.0000.6790.1360.3020.1780.1090.3580.2001.000

Missing values

2023-12-10T23:50:19.945613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:50:20.244786image/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.
2023-12-10T23:50:20.423313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOFRGHT_CNVNC_QTY_TONMRN
02292010009649108DENSA PUMABULK CARRIER27.8178.015.6<NA>28.8437<NA>20133672201-Jan-2022 00:06:0914-Jul-2022 14:24:49-33.0098-71.592328.087601-86.83460215825002
12292020009649093DENSA HAWKBULK CARRIER27.8178.015.6<NA>28.8625<NA>20133674601-Jan-2022 00:03:2617-Jul-2022 21:55:1352.2024994.2505846.077202-1.2519632258603
22292030009649081DENSA FALCONBULK CARRIER27.8178.015.6<NA>28.8672<NA>20133675201-Jan-2022 07:25:0917-Jul-2022 21:52:5828.0714135.13999938.6982-9.173319696004
32292040009649079DENSA SEA LIONBULK CARRIER27.8178.015.6<NA>28.8774<NA>20133676501-Jan-2022 00:40:0717-Jul-2022 21:58:5710.1567-79.4287037.28333108.95826131505
42292070009476460Mayfair SpiritBulk Carrier38.0222.020.7Unimar Success30.0Jiangsu New YZJ20119325706-Jan-2022 03:58:5905-Jun-2022 22:30:254.12733100.58200157.370319.835810508506
52292120009455686MonemvasiaBulk Carrier45.0282.024.8Minerva Marine17.5503Shanghai Waigaoqiao200917793301-Jan-2022 00:00:5117-Jul-2022 21:59:11-20.087601118.63300315.258365.15329750468107
62292130009625475DOGANBULK CARRIER30.0172.014.7<NA>5.0<NA>20133517301-Jan-2022 00:09:2505-Jul-2022 14:26:5129.89380132.53799832.81000135.03049927045808
72292140009625463NEDIMBULK CARRIER30.0172.014.7<NA>5.0<NA>20133515701-Jan-2022 00:02:3417-Jul-2022 21:51:57-30.094801153.49699436.47330118.34670131598609
82292150009625451ORHANBULK CARRIER30.0172.014.7<NA>5.0<NA>20133513901-Jan-2022 01:10:4917-Jul-2022 16:02:4510.1933-64.783302-25.639999-48.158298110646010
92292180009657789KONYABULK CARRIER32.26193.7418.5<NA>27.2114<NA>20136320001-Jan-2022 00:05:2317-Jul-2022 21:31:05-28.912132.138136.818298-39.646702209755011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOFRGHT_CNVNC_QTY_TONMRN
392293820009479204Thalassini AgathaBulk Carrier45.0287.924.5Enesel SA17.6442Universal SB (Tsu)201118230701-Jan-2022 00:06:4805-Jun-2022 23:11:31-10.056572.727203-28.96229932.129101415698041
402293870009587257HuahineBulk Carrier50.0294.024.9Dryships18.1537SCS Shipbuilding201320603701-Jan-2022 00:04:2217-Jul-2022 21:46:37-33.926701113.658997-0.652322-8.46894671046042
412293950009490777JS BANDOLBULK CARRIER32.26185.018.0<NA>5.0<NA>20105800001-Jan-2022 15:18:2517-Jul-2022 13:56:0031.606431.743732.614399127.727997224627043
422293960009490868JS POMEROLBULK CARRIER32.26185.3418.0<NA>4.0<NA>20115800001-Jan-2022 00:38:5917-Jul-2022 21:48:369.93235-61.61836.9907126.724998236219044
432293970009445772SeaunityBulk Carrier45.0283.824.7Thenamaris17.6239Imabari SB Saijo201018136008-Jan-2022 05:52:0005-Jun-2022 23:57:4931.240801122.036003-20.0308118.529999234159045
442294090009721671TopekaBulk Carrier46.0285.024.8Dryships17.585Beihai Shipyard201517954901-Jan-2022 00:08:0905-Jun-2022 23:59:24-20.385799116.57-19.856701118.538002572878046
452294100009662332CANOBULK CARRIER28.6178.015.6<NA>30.0<NA>20133898001-Jan-2022 00:37:5617-Jul-2022 21:35:31-10.0801-78.187698-33.639599-71.685501231140047
462294110009662320DODOBULK CARRIER28.6178.015.6<NA>9.0<NA>20133901701-Jan-2022 00:34:2917-Jul-2022 19:33:35-7.41787-81.23560320.1679-72.620399138913048
472294210009118678ElefsisBulk Carrier32.2217.019.0Eastern Med30.0CSBC (Keelung)19977287301-Jan-2022 18:17:2317-Jul-2022 21:14:1429.756149.538502-24.3557-38.75130168813349
482294220009721683OregonBulk Carrier46.0285.024.8Dryships17.5876Beihai Shipyard201517966701-Jan-2022 00:19:2405-Jun-2022 23:33:08-9.63333115.7777.57667110.672997479352050