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_001071

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 4 other fieldsHigh correlation
SHIP_LNTH is highly overall correlated with SHIP_WDTH and 4 other fieldsHigh correlation
SHIP_HGHT is highly overall correlated with SHIP_WDTH and 4 other fieldsHigh correlation
BULD_YR is highly overall correlated with IMO_IDNTF_NOHigh correlation
DDWGHT is highly overall correlated with SHIP_WDTH and 4 other fieldsHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with SHIP_WDTH and 4 other fieldsHigh correlation
RN is highly overall correlated with MMSIHigh correlation
SHIP_KIND is highly overall correlated with SHIP_WDTH and 4 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 has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:43:10.116402
Analysis finished2023-12-10 14:43:30.227933
Duration20.11 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%
Mean2.2931231 × 108
Minimum2.29203 × 108
Maximum2.29478 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:30.291097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.29203 × 108
5-th percentile2.29209 × 108
Q12.29243 × 108
median2.29287 × 108
Q32.29381 × 108
95-th percentile2.294216 × 108
Maximum2.29478 × 108
Range275000
Interquartile range (IQR)138000

Descriptive statistics

Standard deviation78376.815
Coefficient of variation (CV)0.00034179071
Kurtosis-1.1503663
Mean2.2931231 × 108
Median Absolute Deviation (MAD)72000
Skewness0.23105391
Sum1.1236303 × 1010
Variance6.1429252 × 109
MonotonicityStrictly increasing
2023-12-10T23:43:30.414117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
229203000 1
 
2.0%
229382000 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%
229373000 1
 
2.0%
229376000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
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%
229219000 1
2.0%
229220000 1
2.0%
ValueCountFrequency (%)
229478000 1
2.0%
229462000 1
2.0%
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%

IMO_IDNTF_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9545126.2
Minimum9118678
Maximum9721683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:30.538128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9118678
5-th percentile9218878
Q19476460
median9608702
Q39644201
95-th percentile9662378.2
Maximum9721683
Range603005
Interquartile range (IQR)167741

Descriptive statistics

Standard deviation147090.57
Coefficient of variation (CV)0.015410018
Kurtosis1.4304406
Mean9545126.2
Median Absolute Deviation (MAD)41469
Skewness-1.452087
Sum4.6771119 × 108
Variance2.1635636 × 1010
MonotonicityNot monotonic
2023-12-10T23:43:30.684484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9649081 1
 
2.0%
9479204 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%
9616905 1
 
2.0%
9643908 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%
9323900 1
2.0%
9445772 1
2.0%
9455686 1
2.0%
9457854 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%
9650171 1
2.0%
9649081 1
2.0%
9649079 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:43:30.922801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length9.2040816
Min length4

Characters and Unicode

Total characters451
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 FALCON
2nd rowDENSA SEA LION
3rd rowMayfair Spirit
4th rowMonemvasia
5th rowDOGAN
ValueCountFrequency (%)
densa 2
 
2.8%
js 2
 
2.8%
lbc 2
 
2.8%
flag 2
 
2.8%
nba 2
 
2.8%
confidence 1
 
1.4%
magritte 1
 
1.4%
levante 1
 
1.4%
schinousa 1
 
1.4%
first 1
 
1.4%
Other values (57) 57
79.2%
2023-12-10T23:43:31.262254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 32
 
7.1%
a 25
 
5.5%
23
 
5.1%
i 22
 
4.9%
n 21
 
4.7%
e 21
 
4.7%
N 20
 
4.4%
S 19
 
4.2%
E 19
 
4.2%
o 18
 
4.0%
Other values (37) 231
51.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 239
53.0%
Lowercase Letter 189
41.9%
Space Separator 23
 
5.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 32
13.4%
N 20
 
8.4%
S 19
 
7.9%
E 19
 
7.9%
O 18
 
7.5%
L 18
 
7.5%
I 15
 
6.3%
B 12
 
5.0%
R 12
 
5.0%
M 11
 
4.6%
Other values (14) 63
26.4%
Lowercase Letter
ValueCountFrequency (%)
a 25
13.2%
i 22
11.6%
n 21
11.1%
e 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 (%)
23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 428
94.9%
Common 23
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 32
 
7.5%
a 25
 
5.8%
i 22
 
5.1%
n 21
 
4.9%
e 21
 
4.9%
N 20
 
4.7%
S 19
 
4.4%
E 19
 
4.4%
o 18
 
4.2%
O 18
 
4.2%
Other values (36) 213
49.8%
Common
ValueCountFrequency (%)
23
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 32
 
7.1%
a 25
 
5.5%
23
 
5.1%
i 22
 
4.9%
n 21
 
4.7%
e 21
 
4.7%
N 20
 
4.4%
S 19
 
4.2%
E 19
 
4.2%
o 18
 
4.0%
Other values (37) 231
51.2%

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

2023-12-10T23:43:31.379135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:43:31.462451image/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%
Mean35.085714
Minimum23.5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:31.541042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile28
Q132
median32.26
Q338
95-th percentile46
Maximum50
Range26.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.7314931
Coefficient of variation (CV)0.19185852
Kurtosis-0.5277025
Mean35.085714
Median Absolute Deviation (MAD)3.66
Skewness0.79700811
Sum1719.2
Variance45.313
MonotonicityNot monotonic
2023-12-10T23:43:31.639345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
32.26 15
30.6%
45.0 6
 
12.2%
38.0 3
 
6.1%
30.0 3
 
6.1%
32.2 3
 
6.1%
27.8 2
 
4.1%
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 2
 
4.1%
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 15
30.6%
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 15
30.6%
32.2 3
 
6.1%
32.0 2
 
4.1%
30.0 3
 
6.1%

SHIP_LNTH
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.04735
Minimum168.5
Maximum294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:31.732746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168.5
5-th percentile172
Q1183
median215
Q3225.5
95-th percentile286.74
Maximum294
Range125.5
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation40.46534
Coefficient of variation (CV)0.18643554
Kurtosis-0.7735311
Mean217.04735
Median Absolute Deviation (MAD)30.62
Skewness0.73234539
Sum10635.32
Variance1637.4438
MonotonicityNot monotonic
2023-12-10T23:43:31.830400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
178.0 4
 
8.2%
193.74 4
 
8.2%
217.0 4
 
8.2%
222.0 4
 
8.2%
225.5 4
 
8.2%
282.0 3
 
6.1%
185.0 3
 
6.1%
172.0 3
 
6.1%
205.0 2
 
4.1%
294.0 2
 
4.1%
Other values (14) 16
32.7%
ValueCountFrequency (%)
168.5 1
 
2.0%
171.5 1
 
2.0%
172.0 3
6.1%
176.0 1
 
2.0%
178.0 4
8.2%
181.0 1
 
2.0%
183.0 2
4.1%
185.0 3
6.1%
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 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.22551
Minimum14.1
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:31.933777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.1
5-th percentile14.43
Q116.5
median18.6
Q320.7
95-th percentile24.8
Maximum24.9
Range10.8
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.4285621
Coefficient of variation (CV)0.178334
Kurtosis-0.79412133
Mean19.22551
Median Absolute Deviation (MAD)2.1
Skewness0.37571282
Sum942.05
Variance11.755038
MonotonicityNot monotonic
2023-12-10T23:43:32.032650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
20.05 5
 
10.2%
24.8 5
 
10.2%
18.5 5
 
10.2%
15.6 4
 
8.2%
20.7 3
 
6.1%
18.0 3
 
6.1%
14.7 3
 
6.1%
17.7 2
 
4.1%
14.1 2
 
4.1%
24.9 2
 
4.1%
Other values (15) 15
30.6%
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 4
8.2%
16.5 1
 
2.0%
17.7 2
4.1%
18.0 3
6.1%
18.1 1
 
2.0%
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
2023-12-10T23:43:32.194632image/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%
2023-12-10T23:43:32.496820image/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%
Mean23.095249
Minimum3
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:32.871297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q117.5876
median27.2114
Q330
95-th percentile30
Maximum30
Range27
Interquartile range (IQR)12.4124

Descriptive statistics

Standard deviation8.8055942
Coefficient of variation (CV)0.38127297
Kurtosis0.036663846
Mean23.095249
Median Absolute Deviation (MAD)2.7886
Skewness-1.1332219
Sum1131.6672
Variance77.538489
MonotonicityNot monotonic
2023-12-10T23:43:32.970151image/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%
28.8672 1
 
2.0%
24.3378 1
 
2.0%
27.3311 1
 
2.0%
24.4033 1
 
2.0%
17.5876 1
 
2.0%
9.0 1
 
2.0%
17.585 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%
27.3311 1
 
2.0%
27.2114 4
 
8.2%
25.2942 1
 
2.0%
24.4033 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
2023-12-10T23:43:33.125265image/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%
2023-12-10T23:43:33.388217image/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 

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.102
Minimum1997
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:33.490600image/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 deviation4.1293911
Coefficient of variation (CV)0.0020532976
Kurtosis5.3660691
Mean2011.102
Median Absolute Deviation (MAD)0
Skewness-2.4188235
Sum98544
Variance17.051871
MonotonicityNot monotonic
2023-12-10T23:43:33.593998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2013 27
55.1%
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 (3) 3
 
6.1%
ValueCountFrequency (%)
1997 1
 
2.0%
1998 2
 
4.1%
2003 1
 
2.0%
2005 1
 
2.0%
2007 1
 
2.0%
2008 1
 
2.0%
2009 1
 
2.0%
2010 2
 
4.1%
2011 4
8.2%
2012 5
10.2%
ValueCountFrequency (%)
2015 2
 
4.1%
2014 1
 
2.0%
2013 27
55.1%
2012 5
 
10.2%
2011 4
 
8.2%
2010 2
 
4.1%
2009 1
 
2.0%
2008 1
 
2.0%
2007 1
 
2.0%
2005 1
 
2.0%

DDWGHT
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87384.469
Minimum27780
Maximum206046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:33.726579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27780
5-th percentile33486.6
Q148913
median71663
Q393283
95-th percentile181928.2
Maximum206046
Range178266
Interquartile range (IQR)44370

Descriptive statistics

Standard deviation53667.057
Coefficient of variation (CV)0.61414869
Kurtosis-0.20683285
Mean87384.469
Median Absolute Deviation (MAD)22750
Skewness1.0506433
Sum4281839
Variance2.8801531 × 109
MonotonicityNot monotonic
2023-12-10T23:43:33.844497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
63200 4
 
8.2%
58000 2
 
4.1%
206037 1
 
2.0%
82099 1
 
2.0%
93207 1
 
2.0%
176247 1
 
2.0%
71663 1
 
2.0%
76847 1
 
2.0%
56526 1
 
2.0%
75884 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%
36752 1
2.0%
36765 1
2.0%
37003 1
2.0%
37009 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
2023-12-10T23:43:33.983640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.185933image/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
2023-12-10T23:43:34.378205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:34.528825image/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%
Mean8.4885515
Minimum-38.784401
Maximum56.5882
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)38.8%
Memory size573.0 B
2023-12-10T23:43:34.695981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-38.784401
5-th percentile-34.49122
Q1-9.63333
median10.1567
Q329.893801
95-th percentile46.679821
Maximum56.5882
Range95.372601
Interquartile range (IQR)39.527131

Descriptive statistics

Standard deviation26.93337
Coefficient of variation (CV)3.1729053
Kurtosis-1.0276704
Mean8.4885515
Median Absolute Deviation (MAD)19.79003
Skewness-0.17397968
Sum415.93902
Variance725.40641
MonotonicityNot monotonic
2023-12-10T23:43:34.848213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
28.0714 1
 
2.0%
-10.0565 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%
-34.741501 1
 
2.0%
22.451401 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%
-30.094801 1
2.0%
-28.9121 1
2.0%
-20.385799 1
2.0%
-20.087601 1
2.0%
ValueCountFrequency (%)
56.5882 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%
37.4659 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%
Mean20.076502
Minimum-150.99699
Maximum153.49699
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)34.7%
Memory size573.0 B
2023-12-10T23:43:34.993181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-150.99699
5-th percentile-114.09832
Q1-61.618
median32.020599
Q3104.36
95-th percentile121.8744
Maximum153.49699
Range304.49399
Interquartile range (IQR)165.978

Descriptive statistics

Standard deviation83.706881
Coefficient of variation (CV)4.1693957
Kurtosis-1.1085345
Mean20.076502
Median Absolute Deviation (MAD)80.773899
Skewness-0.29547374
Sum983.7486
Variance7006.842
MonotonicityNot monotonic
2023-12-10T23:43:35.109171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
135.139999 1
 
2.0%
72.727203 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%
-57.805801 1
 
2.0%
-97.757797 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%
Mean7.6254215
Minimum-35.942799
Maximum59.7024
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)38.8%
Memory size573.0 B
2023-12-10T23:43:35.246947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-35.942799
5-th percentile-34.042319
Q1-20.0308
median7.57667
Q332.711601
95-th percentile47.07894
Maximum59.7024
Range95.645199
Interquartile range (IQR)52.742401

Descriptive statistics

Standard deviation27.96155
Coefficient of variation (CV)3.6668858
Kurtosis-1.2384885
Mean7.6254215
Median Absolute Deviation (MAD)27.51047
Skewness-0.032400946
Sum373.64565
Variance781.84829
MonotonicityNot monotonic
2023-12-10T23:43:35.378425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
38.6982 1
 
2.0%
-28.962299 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%
1.28721 1
 
2.0%
-34.310799 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%
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%
36.473301 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.769075
Minimum-152.45799
Maximum151.46001
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:43:35.518212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-152.45799
5-th percentile-90.024939
Q1-38.751301
median31.9053
Q3108.958
95-th percentile135.003
Maximum151.46001
Range303.918
Interquartile range (IQR)147.7093

Descriptive statistics

Standard deviation78.137614
Coefficient of variation (CV)2.716028
Kurtosis-0.97930643
Mean28.769075
Median Absolute Deviation (MAD)72.033703
Skewness-0.18516322
Sum1409.6847
Variance6105.4867
MonotonicityNot monotonic
2023-12-10T23:43:35.657187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-9.1733 1
 
2.0%
32.129101 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%
103.939003 1
 
2.0%
-51.970299 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%
-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%
-48.158298 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
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3225914 × 109
Minimum5.1719 × 108
Maximum6.10402 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:43:35.793766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.1719 × 108
5-th percentile7.34539 × 108
Q11.26299 × 109
median1.73954 × 109
Q32.87695 × 109
95-th percentile5.42008 × 109
Maximum6.10402 × 109
Range5.58683 × 109
Interquartile range (IQR)1.61396 × 109

Descriptive statistics

Standard deviation1.4874135 × 109
Coefficient of variation (CV)0.6404112
Kurtosis0.12384861
Mean2.3225914 × 109
Median Absolute Deviation (MAD)7.6927 × 108
Skewness1.0621928
Sum1.1380698 × 1011
Variance2.212399 × 1018
MonotonicityNot monotonic
2023-12-10T23:43:35.956750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1262990000 1
 
2.0%
3798650000 1
 
2.0%
2667670000 1
 
2.0%
2572360000 1
 
2.0%
2409860000 1
 
2.0%
966068000 1
 
2.0%
5359210000 1
 
2.0%
1325830000 1
 
2.0%
2606200000 1
 
2.0%
1862340000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
517190000 1
2.0%
632096000 1
2.0%
641019000 1
2.0%
874819000 1
2.0%
915699000 1
2.0%
944557000 1
2.0%
966068000 1
2.0%
1061390000 1
2.0%
1102110000 1
2.0%
1176700000 1
2.0%
ValueCountFrequency (%)
6104020000 1
2.0%
5670730000 1
2.0%
5460660000 1
2.0%
5359210000 1
2.0%
4957750000 1
2.0%
4619110000 1
2.0%
4311940000 1
2.0%
4178410000 1
2.0%
3798650000 1
2.0%
3680360000 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
2023-12-10T23:43:36.102475image/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
2023-12-10T23:43:36.274190image/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

2023-12-10T23:43:28.740114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:10.821289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.976991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.170770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.331858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:15.602273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.171053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.325006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.496362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:20.640189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.948344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:23.142696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.815122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.302010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.815792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:10.912822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.059409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.249730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.409394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:15.979084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.255148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.405457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.583755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:20.710852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.029266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:23.339930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.912301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.415863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.886164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.009166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.136974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.338444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.502555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.064396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.340906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.491109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.665912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.069762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.117599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:23.667122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.004075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.508741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.951242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.109211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.226843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.412058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.603545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.138902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.428274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.568275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.748379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.138594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.216663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:23.922907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.096153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.586157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.027982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.195122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.317046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.488681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.682007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.216061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.521015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.664133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.827507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.212463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.314467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:24.229789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.251928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.670683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.099838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.287840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.398328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.563766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.758724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.304861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.602174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.744689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.903163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.280945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.394926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:24.580933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.367651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.754408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.164458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.365219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.483562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.635808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.869965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.383603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.691270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.823797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.978307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.351516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.475183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:24.818802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.457656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.112809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.237050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.447457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.597477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.723752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.971398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.476205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.781060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.907031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:20.066272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.430115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.563944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:24.967823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.562097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.190314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.321919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.523819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.691374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.816053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:15.076092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.606516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.863055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.992555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:20.146656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.510745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.647942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.155593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.656525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.277895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.388393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.597038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.767938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.905811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:15.157820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.706114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.938137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.071352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:20.229462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.580607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.729178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.314702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.760958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.348510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.462478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.671672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.859132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.987715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:15.247893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.815463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.014856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.156177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:20.312788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.655362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.812727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.438694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.847727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.429257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.538731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.751783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:12.936151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.077312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:15.326983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:16.901345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.086441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.236559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:20.389967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.725017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.891347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.531137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:26.945976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.513837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.614746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.843597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.014636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.171849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:15.425334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.006807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.170356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.319671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:20.477560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.801077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:22.974197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.615555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.047512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.595149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:29.693534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:11.907967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:13.092692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:14.254269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:15.510895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:17.089485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:18.249334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:19.407790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:20.565064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:21.877672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:23.062653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:25.705345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:27.137075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:43:28.666729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:43:36.391016image/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_QTYRN
MMSI1.0000.5201.0000.2840.4120.3350.0000.8510.5420.6320.6680.0001.0001.0000.0000.0000.0000.7470.0000.904
IMO_IDNTF_NO0.5201.0001.0000.0000.6800.5070.7320.7010.4280.9120.8850.4311.0001.0000.5460.0000.3540.0570.2790.510
SHIP_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
SHIP_KIND0.2840.0001.0001.0000.9880.7760.7770.0000.5650.6100.0000.7491.0001.0000.5440.4490.2410.2330.7410.452
SHIP_WDTH0.4120.6801.0000.9881.0000.8470.8720.6090.7930.8150.5980.9371.0001.0000.5480.0000.0820.4000.7960.211
SHIP_LNTH0.3350.5071.0000.7760.8471.0000.8440.6530.8930.8080.5070.9801.0001.0000.2530.4540.0000.2710.7260.490
SHIP_HGHT0.0000.7321.0000.7770.8720.8441.0000.9290.7370.9140.7230.8181.0001.0000.0000.3750.0000.5050.6250.489
SHIP_OWNER_NM0.8510.7011.0000.0000.6090.6530.9291.0000.8710.9350.8870.0001.0001.0000.0000.7810.7520.0000.4430.850
DRAFT0.5420.4281.0000.5650.7930.8930.7370.8711.0001.0000.6310.7161.0001.0000.1140.2690.2070.3290.4560.379
SHPYRD_NM0.6320.9121.0000.6100.8150.8080.9140.9351.0001.0000.8770.5721.0001.0000.0000.7500.7040.1700.6900.718
BULD_YR0.6680.8851.0000.0000.5980.5070.7230.8870.6310.8771.0000.3341.0001.0000.3450.0000.0000.2380.2210.252
DDWGHT0.0000.4311.0000.7490.9370.9800.8180.0000.7160.5720.3341.0001.0001.0000.3130.4340.0000.2400.8630.346
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_LA0.0000.5461.0000.5440.5480.2530.0000.0000.1140.0000.3450.3131.0001.0001.0000.2520.3700.0000.5560.000
DPTRP_LO0.0000.0001.0000.4490.0000.4540.3750.7810.2690.7500.0000.4341.0001.0000.2521.0000.0000.0000.0000.000
DTNT_LA0.0000.3541.0000.2410.0820.0000.0000.7520.2070.7040.0000.0001.0001.0000.3700.0001.0000.6330.3270.417
DTNT_LO0.7470.0571.0000.2330.4000.2710.5050.0000.3290.1700.2380.2401.0001.0000.0000.0000.6331.0000.4490.373
FRGHT_CNVNC_QTY0.0000.2791.0000.7410.7960.7260.6250.4430.4560.6900.2210.8631.0001.0000.5560.0000.3270.4491.0000.000
RN0.9040.5101.0000.4520.2110.4900.4890.8500.3790.7180.2520.3461.0001.0000.0000.0000.4170.3730.0001.000
2023-12-10T23:43:36.601616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MMSIIMO_IDNTF_NOSHIP_WDTHSHIP_LNTHSHIP_HGHTDRAFTBULD_YRDDWGHTDPTRP_LADPTRP_LODTNT_LADTNT_LOFRGHT_CNVNC_QTYRNSHIP_KIND
MMSI1.000-0.1280.2630.2420.2300.035-0.1150.253-0.025-0.045-0.4200.0150.1941.0000.197
IMO_IDNTF_NO-0.1281.000-0.034-0.050-0.061-0.1680.831-0.072-0.292-0.0460.1960.0080.136-0.1280.000
SHIP_WDTH0.263-0.0341.0000.8980.889-0.0470.0310.923-0.1920.337-0.1110.4090.8060.2630.806
SHIP_LNTH0.242-0.0500.8981.0000.9650.0880.0740.987-0.1840.305-0.1690.4310.8150.2420.687
SHIP_HGHT0.230-0.0610.8890.9651.0000.0620.0820.975-0.1920.277-0.1680.3730.7500.2300.651
DRAFT0.035-0.168-0.0470.0880.0621.000-0.2240.0960.170-0.166-0.291-0.019-0.1210.0350.432
BULD_YR-0.1150.8310.0310.0740.082-0.2241.0000.043-0.404-0.0180.1160.0600.255-0.1150.000
DDWGHT0.253-0.0720.9230.9870.9750.0960.0431.000-0.1840.308-0.1620.4090.7900.2530.651
DPTRP_LA-0.025-0.292-0.192-0.184-0.1920.170-0.404-0.1841.000-0.274-0.145-0.200-0.157-0.0250.259
DPTRP_LO-0.045-0.0460.3370.3050.277-0.166-0.0180.308-0.2741.0000.0600.1060.211-0.0450.291
DTNT_LA-0.4200.196-0.111-0.169-0.168-0.2910.116-0.162-0.1450.0601.0000.045-0.079-0.4200.121
DTNT_LO0.0150.0080.4090.4310.373-0.0190.0600.409-0.2000.1060.0451.0000.4670.0150.090
FRGHT_CNVNC_QTY0.1940.1360.8060.8150.750-0.1210.2550.790-0.1570.211-0.0790.4671.0000.1940.558
RN1.000-0.1280.2630.2420.2300.035-0.1150.253-0.025-0.045-0.4200.0150.1941.0000.299
SHIP_KIND0.1970.0000.8060.6870.6510.4320.0000.6510.2590.2910.1210.0900.5580.2991.000

Missing values

2023-12-10T23:43:29.850034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:43:30.048781image/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.
2023-12-10T23:43:30.182259image/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_QTYRN
02292030009649081DENSA FALCONBULK CARRIER27.8178.015.6<NA>28.8672<NA>20133675201-Jan-2022 07:25:0917-Jul-2022 21:52:5828.0714135.13999938.6982-9.173312629900002
12292040009649079DENSA SEA LIONBULK CARRIER27.8178.015.6<NA>28.8774<NA>20133676501-Jan-2022 00:40:0717-Jul-2022 21:58:5710.1567-79.4287037.28333108.95811767000003
22292070009476460Mayfair SpiritBulk Carrier38.0222.020.7Unimar Success30.0Jiangsu New YZJ20119325706-Jan-2022 03:58:5905-Jun-2022 22:30:254.12733100.58200157.370319.83586410190004
32292120009455686MonemvasiaBulk Carrier45.0282.024.8Minerva Marine17.5503Shanghai Waigaoqiao200917793301-Jan-2022 00:00:5117-Jul-2022 21:59:11-20.087601118.63300315.258365.15329743119400005
42292130009625475DOGANBULK CARRIER30.0172.014.7<NA>5.0<NA>20133517301-Jan-2022 00:09:2505-Jul-2022 14:26:5129.89380132.53799832.81000135.03049911860600006
52292140009625463NEDIMBULK CARRIER30.0172.014.7<NA>5.0<NA>20133515701-Jan-2022 00:02:3417-Jul-2022 21:51:57-30.094801153.49699436.47330118.34670112650200007
62292150009625451ORHANBULK CARRIER30.0172.014.7<NA>5.0<NA>20133513901-Jan-2022 01:10:4917-Jul-2022 16:02:4510.1933-64.783302-25.639999-48.1582986320960008
72292180009657789KONYABULK CARRIER32.26193.7418.5<NA>27.2114<NA>20136320001-Jan-2022 00:05:2317-Jul-2022 21:31:05-28.912132.138136.818298-39.64670216763500009
82292190009644201IZMIRBULK CARRIER32.0193.7418.5<NA>27.2114<NA>20136320001-Jan-2022 01:54:4817-Jul-2022 21:58:4830.0532-90.50630220.87669972.595001129645000010
92292200009657777SAMSUNBULK CARRIER32.26193.7418.5<NA>27.2114<NA>20136320001-Jan-2022 16:43:4517-Jul-2022 21:44:1719.93580163.21720114.3617-152.457993167696000011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOFRGHT_CNVNC_QTYRN
392293950009490777JS BANDOLBULK CARRIER32.26185.018.0<NA>5.0<NA>20105800001-Jan-2022 15:18:2517-Jul-2022 13:56:0031.606431.743732.614399127.727997154370000041
402293960009490868JS POMEROLBULK CARRIER32.26185.3418.0<NA>4.0<NA>20115800001-Jan-2022 00:38:5917-Jul-2022 21:48:369.93235-61.61836.9907126.724998181642000042
412293970009445772SeaunityBulk Carrier45.0283.824.7Thenamaris17.6239Imabari SB Saijo201018136008-Jan-2022 05:52:0005-Jun-2022 23:57:4931.240801122.036003-20.0308118.529999368036000043
422294090009721671TopekaBulk Carrier46.0285.024.8Dryships17.585Beihai Shipyard201517954901-Jan-2022 00:08:0905-Jun-2022 23:59:24-20.385799116.57-19.856701118.538002417841000044
432294100009662332CANOBULK CARRIER28.6178.015.6<NA>30.0<NA>20133898001-Jan-2022 00:37:5617-Jul-2022 21:35:31-10.0801-78.187698-33.639599-71.685501118969000045
442294110009662320DODOBULK CARRIER28.6178.015.6<NA>9.0<NA>20133901701-Jan-2022 00:34:2917-Jul-2022 19:33:35-7.41787-81.23560320.1679-72.62039991569900046
452294210009118678ElefsisBulk Carrier32.2217.019.0Eastern Med30.0CSBC (Keelung)19977287301-Jan-2022 18:17:2317-Jul-2022 21:14:1429.756149.538502-24.3557-38.75130187481900047
462294220009721683OregonBulk Carrier46.0285.024.8Dryships17.5876Beihai Shipyard201517966701-Jan-2022 00:19:2405-Jun-2022 23:33:08-9.63333115.7777.57667110.672997461911000048
472294620009323900ELEOUSSABULK CARRIER32.26185.018.1<NA>24.4033<NA>20085667801-Jan-2022 05:03:1117-Jul-2022 21:33:1737.46594.17491-19.9338-26.549101152040000049
482294780009650171KIRAN ANATOLIABULK CARRIER32.26194.518.5<NA>27.3311<NA>20136347801-Jan-2022 00:09:0517-Jul-2022 21:35:08-1.51176-48.753332.711601-29.700701139809000050