Overview

Dataset statistics

Number of variables20
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory176.7 B

Variable types

Numeric14
Text1
Categorical3
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001305

Alerts

MMSI is highly overall correlated with IMO_IDNTF_NO and 4 other fieldsHigh correlation
IMO_IDNTF_NO is highly overall correlated with MMSI and 2 other fieldsHigh correlation
SHIP_WDTH is highly overall correlated with SHIP_LNTH and 6 other fieldsHigh correlation
SHIP_LNTH is highly overall correlated with MMSI and 7 other fieldsHigh correlation
SHIP_HGHT is highly overall correlated with SHIP_WDTH and 5 other fieldsHigh correlation
DRAFT is highly overall correlated with SHIP_WDTH and 6 other fieldsHigh correlation
BULD_YR is highly overall correlated with RN and 1 other fieldsHigh correlation
DDWGHT is highly overall correlated with MMSI and 6 other fieldsHigh correlation
DPTRP_LA is highly overall correlated with SHIP_KINDHigh correlation
FRGHT_CNVNC_QTY is highly overall correlated with SHIP_OWNER_NMHigh correlation
RN is highly overall correlated with BULD_YRHigh correlation
SHIP_KIND is highly overall correlated with SHIP_WDTH and 5 other fieldsHigh correlation
SHIP_OWNER_NM is highly overall correlated with MMSI and 9 other fieldsHigh correlation
SHPYRD_NM is highly overall correlated with MMSI and 9 other fieldsHigh correlation
SHIP_KIND is highly imbalanced (75.4%)Imbalance
MMSI has unique valuesUnique
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM 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 14:28:20.853541
Analysis finished2023-12-10 14:28:38.173929
Duration17.32 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%
Mean5.531581 × 108
Minimum5.5136169 × 108
Maximum5.5965733 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:28:38.248095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.5136169 × 108
5-th percentile5.5166131 × 108
Q15.5169193 × 108
median5.5353933 × 108
Q35.5374033 × 108
95-th percentile5.5383233 × 108
Maximum5.5965733 × 108
Range8295640
Interquartile range (IQR)2048400

Descriptive statistics

Standard deviation1329033.8
Coefficient of variation (CV)0.0024026292
Kurtosis10.917079
Mean5.531581 × 108
Median Absolute Deviation (MAD)209000
Skewness2.108488
Sum2.7104747 × 1010
Variance1.7663308 × 1012
MonotonicityNot monotonic
2023-12-10T23:28:38.385378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
553382333 1
 
2.0%
551662933 1
 
2.0%
553836333 1
 
2.0%
553826333 1
 
2.0%
553828333 1
 
2.0%
553829333 1
 
2.0%
553820333 1
 
2.0%
553821333 1
 
2.0%
559657333 1
 
2.0%
551361693 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
551361693 1
2.0%
551606933 1
2.0%
551660233 1
2.0%
551662933 1
2.0%
551668233 1
2.0%
551672933 1
2.0%
551673233 1
2.0%
551675233 1
2.0%
551686933 1
2.0%
551687933 1
2.0%
ValueCountFrequency (%)
559657333 1
2.0%
553836333 1
2.0%
553834333 1
2.0%
553829333 1
2.0%
553828333 1
2.0%
553826333 1
2.0%
553821333 1
2.0%
553820333 1
2.0%
553749333 1
2.0%
553748333 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2567984
Minimum2032542
Maximum2783971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:28:38.493850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2032542
5-th percentile2132164.8
Q12578003
median2593792
Q32654869
95-th percentile2776359.4
Maximum2783971
Range751429
Interquartile range (IQR)76866

Descriptive statistics

Standard deviation181754.65
Coefficient of variation (CV)0.070777172
Kurtosis2.501042
Mean2567984
Median Absolute Deviation (MAD)61077
Skewness-1.7945488
Sum1.2583122 × 108
Variance3.3034752 × 1010
MonotonicityNot monotonic
2023-12-10T23:28:38.607987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2578077 1
 
2.0%
2783971 1
 
2.0%
2654883 1
 
2.0%
2682335 1
 
2.0%
2682359 1
 
2.0%
2682361 1
 
2.0%
2682373 1
 
2.0%
2682385 1
 
2.0%
2695667 1
 
2.0%
2162543 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2032542 1
2.0%
2132158 1
2.0%
2132160 1
2.0%
2132172 1
2.0%
2132184 1
2.0%
2162543 1
2.0%
2522016 1
2.0%
2522028 1
2.0%
2522133 1
2.0%
2522951 1
2.0%
ValueCountFrequency (%)
2783971 1
2.0%
2783969 1
2.0%
2783957 1
2.0%
2764963 1
2.0%
2695667 1
2.0%
2682385 1
2.0%
2682373 1
2.0%
2682361 1
2.0%
2682359 1
2.0%
2682335 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:28:38.851127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.795918
Min length10

Characters and Unicode

Total characters627
Distinct characters35
Distinct categories2 ?
Distinct scripts1 ?
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 rowFkduorwwhPdhuvn
2nd rowFruqholdPdhuvn
3rd rowFroxpelqhPdhuvn
4th rowFohphqwlqhPdhuvn
5th rowDahoPdhuvn
ValueCountFrequency (%)
fkduorwwhpdhuvn 1
 
2.0%
hoobpdhuvn 1
 
2.0%
hxjhqpdhuvn 1
 
2.0%
jhuqhupdhuvn 1
 
2.0%
jxqkloghpdhuvn 1
 
2.0%
jxvwdypdhuvn 1
 
2.0%
jxwkruppdhuvn 1
 
2.0%
jhugdpdhuvn 1
 
2.0%
pduiuhwjxbdqh 1
 
2.0%
fpdfjpvruerqqh 1
 
2.0%
Other values (39) 39
79.6%
2023-12-10T23:28:39.163172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 69
 
11.0%
u 68
 
10.8%
d 67
 
10.7%
P 65
 
10.4%
v 38
 
6.1%
F 33
 
5.3%
n 32
 
5.1%
o 26
 
4.1%
w 26
 
4.1%
J 25
 
4.0%
Other values (25) 178
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 449
71.6%
Uppercase Letter 178
 
28.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 69
15.4%
u 68
15.1%
d 67
14.9%
v 38
8.5%
n 32
7.1%
o 26
 
5.8%
w 26
 
5.8%
q 21
 
4.7%
r 21
 
4.7%
l 16
 
3.6%
Other values (13) 65
14.5%
Uppercase Letter
ValueCountFrequency (%)
P 65
36.5%
F 33
18.5%
J 25
 
14.0%
D 22
 
12.4%
H 9
 
5.1%
I 6
 
3.4%
R 4
 
2.2%
O 3
 
1.7%
V 3
 
1.7%
W 3
 
1.7%
Other values (2) 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 627
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 69
 
11.0%
u 68
 
10.8%
d 67
 
10.7%
P 65
 
10.4%
v 38
 
6.1%
F 33
 
5.3%
n 32
 
5.1%
o 26
 
4.1%
w 26
 
4.1%
J 25
 
4.0%
Other values (25) 178
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 69
 
11.0%
u 68
 
10.8%
d 67
 
10.7%
P 65
 
10.4%
v 38
 
6.1%
F 33
 
5.3%
n 32
 
5.1%
o 26
 
4.1%
w 26
 
4.1%
J 25
 
4.0%
Other values (25) 178
28.4%

SHIP_KIND
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Container
47 
Container(add)
 
2

Length

Max length14
Median length9
Mean length9.2040816
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Container 47
95.9%
Container(add) 2
 
4.1%

Length

2023-12-10T23:28:39.268048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:28:39.339342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
container 47
95.9%
container(add 2
 
4.1%

SHIP_WDTH
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.755102
Minimum23
Maximum61.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:28:39.405689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile28.32
Q142.8
median42.8
Q342.8
95-th percentile56.4
Maximum61.3
Range38.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.4576411
Coefficient of variation (CV)0.19781595
Kurtosis0.21507514
Mean42.755102
Median Absolute Deviation (MAD)0
Skewness0.15408003
Sum2095
Variance71.531692
MonotonicityNot monotonic
2023-12-10T23:28:39.515384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
42.8 28
57.1%
56.4 8
 
16.3%
32.2 3
 
6.1%
35.6 3
 
6.1%
27.2 2
 
4.1%
37.3 1
 
2.0%
23.0 1
 
2.0%
61.3 1
 
2.0%
30.0 1
 
2.0%
36.0 1
 
2.0%
ValueCountFrequency (%)
23.0 1
 
2.0%
27.2 2
 
4.1%
30.0 1
 
2.0%
32.2 3
 
6.1%
35.6 3
 
6.1%
36.0 1
 
2.0%
37.3 1
 
2.0%
42.8 28
57.1%
56.4 8
 
16.3%
61.3 1
 
2.0%
ValueCountFrequency (%)
61.3 1
 
2.0%
56.4 8
 
16.3%
42.8 28
57.1%
37.3 1
 
2.0%
36.0 1
 
2.0%
35.6 3
 
6.1%
32.2 3
 
6.1%
30.0 1
 
2.0%
27.2 2
 
4.1%
23.0 1
 
2.0%

SHIP_LNTH
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.80204
Minimum104.4
Maximum393.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:28:39.635586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum104.4
5-th percentile164.76
Q1314.7
median336.4
Q3351.1
95-th percentile376
Maximum393.9
Range289.5
Interquartile range (IQR)36.4

Descriptive statistics

Standard deviation70.522412
Coefficient of variation (CV)0.22763702
Kurtosis0.64726583
Mean309.80204
Median Absolute Deviation (MAD)17.4
Skewness-1.273134
Sum15180.3
Variance4973.4106
MonotonicityNot monotonic
2023-12-10T23:28:39.725790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
351.1 10
20.4%
376.0 8
16.3%
336.4 6
12.2%
331.5 4
 
8.2%
210.0 4
 
8.2%
319.0 3
 
6.1%
314.7 3
 
6.1%
154.6 2
 
4.1%
224.8 2
 
4.1%
104.4 1
 
2.0%
Other values (6) 6
12.2%
ValueCountFrequency (%)
104.4 1
 
2.0%
154.6 2
4.1%
180.0 1
 
2.0%
210.0 4
8.2%
224.0 1
 
2.0%
224.8 2
4.1%
252.4 1
 
2.0%
314.7 3
6.1%
319.0 3
6.1%
328.9 1
 
2.0%
ValueCountFrequency (%)
393.9 1
 
2.0%
376.0 8
16.3%
351.1 10
20.4%
336.4 6
12.2%
333.4 1
 
2.0%
331.5 4
 
8.2%
328.9 1
 
2.0%
319.0 3
 
6.1%
314.7 3
 
6.1%
252.4 1
 
2.0%

SHIP_HGHT
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.863265
Minimum14
Maximum33.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:28:39.812154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15.92
Q124.1
median24.1
Q324.6
95-th percentile30.2
Maximum33.5
Range19.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation4.5145457
Coefficient of variation (CV)0.1891839
Kurtosis-0.06341722
Mean23.863265
Median Absolute Deviation (MAD)0.5
Skewness-0.25306787
Sum1169.3
Variance20.381122
MonotonicityNot monotonic
2023-12-10T23:28:39.901588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
24.1 20
40.8%
30.2 8
 
16.3%
24.6 6
 
12.2%
18.3 4
 
8.2%
18.5 2
 
4.1%
14.0 2
 
4.1%
27.3 2
 
4.1%
18.2 1
 
2.0%
21.4 1
 
2.0%
15.2 1
 
2.0%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
14.0 2
 
4.1%
15.2 1
 
2.0%
17.0 1
 
2.0%
18.2 1
 
2.0%
18.3 4
 
8.2%
18.5 2
 
4.1%
21.4 1
 
2.0%
24.1 20
40.8%
24.6 6
 
12.2%
27.3 2
 
4.1%
ValueCountFrequency (%)
33.5 1
 
2.0%
30.2 8
 
16.3%
27.3 2
 
4.1%
24.6 6
 
12.2%
24.1 20
40.8%
21.4 1
 
2.0%
18.5 2
 
4.1%
18.3 4
 
8.2%
18.2 1
 
2.0%
17.0 1
 
2.0%

SHIP_OWNER_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
Pdhuvn
32 
FPDFJP
12 
3
 
3
Pduiuhw
 
2

Length

Max length7
Median length6
Mean length5.7346939
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Pdhuvn 32
65.3%
FPDFJP 12
 
24.5%
3 3
 
6.1%
Pduiuhw 2
 
4.1%

Length

2023-12-10T23:28:40.018961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:28:40.104023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pdhuvn 32
65.3%
fpdfjp 12
 
24.5%
3 3
 
6.1%
pduiuhw 2
 
4.1%

DRAFT
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.097959
Minimum8.2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:28:40.205915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile10.28
Q114.5
median14.5
Q315
95-th percentile16
Maximum16
Range7.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.8537675
Coefficient of variation (CV)0.13149191
Kurtosis1.403848
Mean14.097959
Median Absolute Deviation (MAD)0.5
Skewness-1.3273508
Sum690.8
Variance3.4364541
MonotonicityNot monotonic
2023-12-10T23:28:40.296234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
14.5 20
40.8%
16.0 11
22.4%
12.0 6
 
12.2%
15.0 6
 
12.2%
9.8 2
 
4.1%
12.5 1
 
2.0%
8.2 1
 
2.0%
11.5 1
 
2.0%
11.0 1
 
2.0%
ValueCountFrequency (%)
8.2 1
 
2.0%
9.8 2
 
4.1%
11.0 1
 
2.0%
11.5 1
 
2.0%
12.0 6
 
12.2%
12.5 1
 
2.0%
14.5 20
40.8%
15.0 6
 
12.2%
16.0 11
22.4%
ValueCountFrequency (%)
16.0 11
22.4%
15.0 6
 
12.2%
14.5 20
40.8%
12.5 1
 
2.0%
12.0 6
 
12.2%
11.5 1
 
2.0%
11.0 1
 
2.0%
9.8 2
 
4.1%
8.2 1
 
2.0%

SHPYRD_NM
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
RghqvhOlqgr
29 
KbxqgdlKLXovdq
FRVFRKLCkrxvkdq
Yronvzhuiw
KbxqgdlVdpkrKL
Other values (4)

Length

Max length15
Median length11
Mean length11.734694
Min length9

Unique

Unique3 ?
Unique (%)6.1%

Sample

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

Common Values

ValueCountFrequency (%)
RghqvhOlqgr 29
59.2%
KbxqgdlKLXovdq 4
 
8.2%
FRVFRKLCkrxvkdq 4
 
8.2%
Yronvzhuiw 3
 
6.1%
KbxqgdlVdpkrKL 3
 
6.1%
VdpvxqjKL 3
 
6.1%
UhprqwrzdUhsdlu 1
 
2.0%
VFVVklsexloglqj 1
 
2.0%
KbxqgdlPlsr 1
 
2.0%

Length

2023-12-10T23:28:40.399453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:28:40.493291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rghqvholqgr 29
59.2%
kbxqgdlklxovdq 4
 
8.2%
frvfrklckrxvkdq 4
 
8.2%
yronvzhuiw 3
 
6.1%
kbxqgdlvdpkrkl 3
 
6.1%
vdpvxqjkl 3
 
6.1%
uhprqwrzduhsdlu 1
 
2.0%
vfvvklsexloglqj 1
 
2.0%
kbxqgdlplsr 1
 
2.0%

BULD_YR
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.3061
Minimum2002
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:28:40.584203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2002
Q12004
median2006
Q32008
95-th percentile2019
Maximum2021
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.9210605
Coefficient of variation (CV)0.0024515745
Kurtosis1.6405567
Mean2007.3061
Median Absolute Deviation (MAD)2
Skewness1.499613
Sum98358
Variance24.216837
MonotonicityNot monotonic
2023-12-10T23:28:40.669955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2003 7
14.3%
2006 7
14.3%
2005 6
12.2%
2007 6
12.2%
2008 5
10.2%
2002 4
8.2%
2019 4
8.2%
2004 3
6.1%
2011 3
6.1%
2009 2
 
4.1%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
2002 4
8.2%
2003 7
14.3%
2004 3
6.1%
2005 6
12.2%
2006 7
14.3%
2007 6
12.2%
2008 5
10.2%
2009 2
 
4.1%
2011 3
6.1%
2015 1
 
2.0%
ValueCountFrequency (%)
2021 1
 
2.0%
2019 4
8.2%
2015 1
 
2.0%
2011 3
6.1%
2009 2
 
4.1%
2008 5
10.2%
2007 6
12.2%
2006 7
14.3%
2005 6
12.2%
2004 3
6.1%

DDWGHT
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104307.12
Minimum8800
Maximum221250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:28:40.963438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8800
5-th percentile23686
Q1101612
median109000
Q3115993
95-th percentile174239
Maximum221250
Range212450
Interquartile range (IQR)14381

Descriptive statistics

Standard deviation48785.359
Coefficient of variation (CV)0.46770879
Kurtosis-0.21098237
Mean104307.12
Median Absolute Deviation (MAD)7190
Skewness-0.013312739
Sum5111049
Variance2.3800112 × 109
MonotonicityNot monotonic
2023-12-10T23:28:41.045459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
115993 10
20.4%
174239 8
16.3%
109000 6
12.2%
38840 4
 
8.2%
41028 3
 
6.1%
104600 3
 
6.1%
101612 3
 
6.1%
21262 2
 
4.1%
113964 2
 
4.1%
101818 2
 
4.1%
Other values (6) 6
12.2%
ValueCountFrequency (%)
8800 1
 
2.0%
21262 2
4.1%
27322 1
 
2.0%
38840 4
8.2%
41028 3
6.1%
63200 1
 
2.0%
101612 3
6.1%
101810 1
 
2.0%
101818 2
4.1%
104600 3
6.1%
ValueCountFrequency (%)
221250 1
 
2.0%
174239 8
16.3%
115993 10
20.4%
113964 2
 
4.1%
109657 1
 
2.0%
109000 6
12.2%
104600 3
 
6.1%
101818 2
 
4.1%
101810 1
 
2.0%
101612 3
 
6.1%
Distinct46
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 00:00:02
Maximum2023-01-01 05:33:15
2023-12-10T23:28:41.139711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:41.249235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-31 23:49:34
Maximum2023-04-30 23:59:57
2023-12-10T23:28:41.353880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:41.462288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

DPTRP_LA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.945144
Minimum-37.973701
Maximum66.941902
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)8.2%
Memory size573.0 B
2023-12-10T23:28:41.579279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-37.973701
5-th percentile-14.93726
Q113.9283
median30.478901
Q336.938702
95-th percentile51.403438
Maximum66.941902
Range104.9156
Interquartile range (IQR)23.010402

Descriptive statistics

Standard deviation20.581232
Coefficient of variation (CV)0.79325948
Kurtosis1.7400148
Mean25.945144
Median Absolute Deviation (MAD)10.719601
Skewness-1.0240991
Sum1271.3121
Variance423.5871
MonotonicityNot monotonic
2023-12-10T23:28:41.718404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-26.7813 1
 
2.0%
35.950401 1
 
2.0%
14.0867 1
 
2.0%
31.9578 1
 
2.0%
36.77 1
 
2.0%
36.787498 1
 
2.0%
40.664902 1
 
2.0%
22.888599 1
 
2.0%
19.7593 1
 
2.0%
50.535599 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-37.973701 1
2.0%
-26.7813 1
2.0%
-24.0564 1
2.0%
-1.25855 1
2.0%
1.76955 1
2.0%
5.99 1
2.0%
8.82977 1
2.0%
10.0783 1
2.0%
11.7161 1
2.0%
12.1016 1
2.0%
ValueCountFrequency (%)
66.941902 1
2.0%
52.657799 1
2.0%
51.981998 1
2.0%
50.535599 1
2.0%
50.0112 1
2.0%
49.596001 1
2.0%
48.34 1
2.0%
44.4184 1
2.0%
43.7206 1
2.0%
40.664902 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6348799
Minimum-160.41701
Maximum144.87199
Zeros0
Zeros (%)0.0%
Negative24
Negative (%)49.0%
Memory size573.0 B
2023-12-10T23:28:41.846761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-160.41701
5-th percentile-113.2826
Q1-74.144798
median1.08916
Q394.616699
95-th percentile127.4328
Maximum144.87199
Range305.289
Interquartile range (IQR)168.7615

Descriptive statistics

Standard deviation86.47443
Coefficient of variation (CV)11.326233
Kurtosis-1.2456886
Mean7.6348799
Median Absolute Deviation (MAD)75.320659
Skewness0.04744012
Sum374.10911
Variance7477.827
MonotonicityNot monotonic
2023-12-10T23:28:41.964035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-95.117699 1
 
2.0%
120.705002 1
 
2.0%
49.9767 1
 
2.0%
125.790001 1
 
2.0%
-75.506699 1
 
2.0%
-75.566704 1
 
2.0%
-74.144798 1
 
2.0%
117.348 1
 
2.0%
-74.231499 1
 
2.0%
1.08916 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-160.417007 1
2.0%
-130.516006 1
2.0%
-118.126999 1
2.0%
-106.015999 1
2.0%
-95.117699 1
2.0%
-90.790001 1
2.0%
-82.133301 1
2.0%
-79.521599 1
2.0%
-75.566704 1
2.0%
-75.506699 1
2.0%
ValueCountFrequency (%)
144.871994 1
2.0%
128.679993 1
2.0%
128.528 1
2.0%
125.790001 1
2.0%
124.955002 1
2.0%
122.800003 1
2.0%
122.523003 1
2.0%
122.288002 1
2.0%
120.921997 1
2.0%
120.705002 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.790251
Minimum-37.698299
Maximum61.307301
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)6.1%
Memory size573.0 B
2023-12-10T23:28:42.080996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-37.698299
5-th percentile-0.904498
Q112.1567
median26.530001
Q335.542702
95-th percentile50.553279
Maximum61.307301
Range99.0056
Interquartile range (IQR)23.386002

Descriptive statistics

Standard deviation19.255312
Coefficient of variation (CV)0.77672921
Kurtosis2.3038087
Mean24.790251
Median Absolute Deviation (MAD)9.957498
Skewness-1.0117332
Sum1214.7223
Variance370.76705
MonotonicityNot monotonic
2023-12-10T23:28:42.197265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8.81153 1
 
2.0%
29.6133 1
 
2.0%
12.1567 1
 
2.0%
30.3633 1
 
2.0%
22.0783 1
 
2.0%
33.321602 1
 
2.0%
10.0267 1
 
2.0%
22.508301 1
 
2.0%
10.3757 1
 
2.0%
35.542702 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-37.698299 1
2.0%
-32.495098 1
2.0%
-2.35273 1
2.0%
1.26785 1
2.0%
6.00225 1
2.0%
7.2282 1
2.0%
8.81153 1
2.0%
8.87042 1
2.0%
9.925 1
2.0%
10.0267 1
2.0%
ValueCountFrequency (%)
61.307301 1
2.0%
54.285 1
2.0%
51.286598 1
2.0%
49.4533 1
2.0%
48.516602 1
2.0%
48.323299 1
2.0%
47.728298 1
2.0%
42.5 1
2.0%
40.669998 1
2.0%
40.006302 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.085168
Minimum-162.907
Maximum142.199
Zeros0
Zeros (%)0.0%
Negative24
Negative (%)49.0%
Memory size573.0 B
2023-12-10T23:28:42.298042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-162.907
5-th percentile-128.7944
Q1-75.876999
median4.2527
Q374.944603
95-th percentile125.6974
Maximum142.199
Range305.106
Interquartile range (IQR)150.8216

Descriptive statistics

Standard deviation89.184933
Coefficient of variation (CV)42.771101
Kurtosis-1.2795449
Mean2.085168
Median Absolute Deviation (MAD)80.129699
Skewness0.0067424499
Sum102.17323
Variance7953.9523
MonotonicityNot monotonic
2023-12-10T23:28:42.409615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-79.5354 1
 
2.0%
-95.008301 1
 
2.0%
72.696701 1
 
2.0%
122.769997 1
 
2.0%
114.232002 1
 
2.0%
26.8172 1
 
2.0%
71.366096 1
 
2.0%
-110.550003 1
 
2.0%
-75.876999 1
 
2.0%
15.7535 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-162.906998 1
2.0%
-153.897003 1
2.0%
-130.360001 1
2.0%
-126.445999 1
2.0%
-110.550003 1
2.0%
-95.008301 1
2.0%
-90.536697 1
2.0%
-84.761703 1
2.0%
-79.987099 1
2.0%
-79.5354 1
2.0%
ValueCountFrequency (%)
142.199005 1
2.0%
132.740005 1
2.0%
127.649002 1
2.0%
122.769997 1
2.0%
122.059998 1
2.0%
122.041 1
2.0%
116.362999 1
2.0%
114.285004 1
2.0%
114.232002 1
2.0%
114.116997 1
2.0%

FRGHT_CNVNC_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2799908 × 108
Minimum23742200
Maximum3.86347 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:28:42.524942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23742200
5-th percentile74983900
Q11.7503 × 108
median2.21312 × 108
Q32.89778 × 108
95-th percentile3.717266 × 108
Maximum3.86347 × 108
Range3.626048 × 108
Interquartile range (IQR)1.14748 × 108

Descriptive statistics

Standard deviation90348466
Coefficient of variation (CV)0.39626679
Kurtosis-0.30671174
Mean2.2799908 × 108
Median Absolute Deviation (MAD)60558000
Skewness-0.15071463
Sum1.1171955 × 1010
Variance8.1628453 × 1015
MonotonicityNot monotonic
2023-12-10T23:28:42.636521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
147305000 1
 
2.0%
296326000 1
 
2.0%
372827000 1
 
2.0%
142029000 1
 
2.0%
182757000 1
 
2.0%
307853000 1
 
2.0%
252061000 1
 
2.0%
327447000 1
 
2.0%
78977500 1
 
2.0%
186072000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
23742200 1
2.0%
25269900 1
2.0%
72321500 1
2.0%
78977500 1
2.0%
119740000 1
2.0%
137666000 1
2.0%
142029000 1
2.0%
147305000 1
2.0%
149246000 1
2.0%
153333000 1
2.0%
ValueCountFrequency (%)
386347000 1
2.0%
374348000 1
2.0%
372827000 1
2.0%
370076000 1
2.0%
366596000 1
2.0%
354159000 1
2.0%
352004000 1
2.0%
347397000 1
2.0%
327447000 1
2.0%
307853000 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
2023-12-10T23:28:42.748346image/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
2023-12-10T23:28:42.858223image/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

2023-12-10T23:28:36.878731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:21.572337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.753041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.863389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.895958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:26.017241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.650161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.965377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.258039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.320414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.670473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.710959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.713571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.671388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.944153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:21.629838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.828986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.934297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.983973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:26.101394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.742050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.062471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.348206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.390564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.760850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.778853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.776702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.735377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.025155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:21.702420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.908863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.016502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.068677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:26.286737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.839009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.170069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.432967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.478033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.845596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.852830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.851017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.815386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.096026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:21.765771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.993917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.093714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.146526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:26.373059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.923276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.272278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.520108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.555799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.920367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.931797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.920589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.887306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.160912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:21.829043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.079479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.172385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.223934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:26.474874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.016568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.366669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.599616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.871828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.990853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.014797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.985988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.965441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.231781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:21.894490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.175032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.249825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.298419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:26.562847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.133876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.460384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.682400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.966722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.064902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.101769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.058521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.238766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.300960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:21.962901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.257768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.323888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.377995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:26.662178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.235987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.548773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.762645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.089375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.142025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.179100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.136038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.310649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.372901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.027946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.341398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.395680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.457829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.042365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.323753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.645670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.842288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.174619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.215273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.253706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.211241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.381774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.431842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.083541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.413061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.463766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.530920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.118985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.405771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.737405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.904922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.241531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.290380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.314516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.271756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.447652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.491168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.402472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.487692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.536909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.600750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.197712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.507374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.828002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.971020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.303830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.372352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.374547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.335953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.518060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.554733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.471406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.561712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.609233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.673974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.284780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.609159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.904417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.039935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.374992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.446724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.437868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.404274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.590713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.616627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.537512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.634644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.675860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.754846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.378483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.701927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:29.977158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.109338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.440888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.510362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.499268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.468968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.660702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.679608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.601447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.712229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.743037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.850163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.462610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.789893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.055688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.177512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.512897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.573737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.568687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.536148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.732981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:37.762967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:22.690710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:23.791209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:24.820187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:25.941545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:27.558224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:28.879579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:30.159216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:31.253043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:32.594038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:33.647282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:34.652869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:35.607990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:28:36.807500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:28:42.945392image/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
MMSI1.0000.6721.0000.0000.8000.9430.5710.7730.5900.8020.5210.8130.9700.9230.0000.0000.1700.0000.5970.808
IMO_IDNTF_NO0.6721.0001.0000.0510.6600.7730.6730.6270.5260.8680.8600.7490.9690.7930.0000.0000.4240.3520.0000.937
SHIP_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
SHIP_KIND0.0000.0511.0001.0000.6710.7170.3600.9481.0000.7170.0000.0001.0001.0000.6840.2500.5750.0000.8290.000
SHIP_WDTH0.8000.6601.0000.6711.0000.9530.9220.7830.9700.9300.8240.9450.9580.9290.6800.2210.6580.3390.7060.738
SHIP_LNTH0.9430.7731.0000.7170.9531.0000.9400.8820.9500.9780.8350.9330.9570.9210.8330.3140.7330.5820.7290.781
SHIP_HGHT0.5710.6731.0000.3600.9220.9401.0000.9140.9120.8840.9380.9610.9180.8340.5010.0000.7200.5960.4820.711
SHIP_OWNER_NM0.7730.6271.0000.9480.7830.8820.9141.0000.8210.8880.7830.7640.9750.9600.4430.2790.5510.2400.8090.693
DRAFT0.5900.5261.0001.0000.9700.9500.9120.8211.0000.7980.5790.8970.9800.9680.6240.0000.6670.0000.7520.636
SHPYRD_NM0.8020.8681.0000.7170.9300.9780.8840.8880.7981.0000.9130.8250.9530.9150.8410.1100.6660.5960.6170.733
BULD_YR0.5210.8601.0000.0000.8240.8350.9380.7830.5790.9131.0000.8140.9810.9680.2990.0000.6150.6040.0000.779
DDWGHT0.8130.7491.0000.0000.9450.9330.9610.7640.8970.8250.8141.0000.9840.9370.1590.0000.4810.2740.4440.792
DPTR_HMS0.9700.9691.0001.0000.9580.9570.9180.9750.9800.9530.9810.9841.0000.9800.0000.8960.9290.8690.9660.804
ARVL_HMS0.9230.7931.0001.0000.9290.9210.8340.9600.9680.9150.9680.9370.9801.0000.9800.8290.0000.0000.7130.869
DPTRP_LA0.0000.0001.0000.6840.6800.8330.5010.4430.6240.8410.2990.1590.0000.9801.0000.6030.5570.5750.4940.510
DPTRP_LO0.0000.0001.0000.2500.2210.3140.0000.2790.0000.1100.0000.0000.8960.8290.6031.0000.5320.7350.5910.000
DTNT_LA0.1700.4241.0000.5750.6580.7330.7200.5510.6670.6660.6150.4810.9290.0000.5570.5321.0000.6530.5380.350
DTNT_LO0.0000.3521.0000.0000.3390.5820.5960.2400.0000.5960.6040.2740.8690.0000.5750.7350.6531.0000.0000.000
FRGHT_CNVNC_QTY0.5970.0001.0000.8290.7060.7290.4820.8090.7520.6170.0000.4440.9660.7130.4940.5910.5380.0001.0000.328
RN0.8080.9371.0000.0000.7380.7810.7110.6930.6360.7330.7790.7920.8040.8690.5100.0000.3500.0000.3281.000
2023-12-10T23:28:43.090649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHPYRD_NMSHIP_KINDSHIP_OWNER_NM
SHPYRD_NM1.0000.6720.755
SHIP_KIND0.6721.0000.776
SHIP_OWNER_NM0.7550.7761.000
2023-12-10T23:28:43.184767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MMSIIMO_IDNTF_NOSHIP_WDTHSHIP_LNTHSHIP_HGHTDRAFTBULD_YRDDWGHTDPTRP_LADPTRP_LODTNT_LADTNT_LOFRGHT_CNVNC_QTYRNSHIP_KINDSHIP_OWNER_NMSHPYRD_NM
MMSI1.0000.5670.2730.5650.0660.059-0.0620.535-0.1040.097-0.1350.1980.144-0.3090.0000.8160.672
IMO_IDNTF_NO0.5671.0000.3000.4180.2470.2410.2230.3580.0460.130-0.0630.0170.030-0.0040.0000.5360.786
SHIP_WDTH0.2730.3001.0000.9010.9310.9120.0370.9010.1680.3150.1100.3510.149-0.1070.6840.6500.816
SHIP_LNTH0.5650.4180.9011.0000.7350.7140.0290.9840.1560.2690.0760.3260.237-0.2290.6720.7440.749
SHIP_HGHT0.0660.2470.9310.7351.0000.9940.0910.7650.1790.3110.1300.2990.1270.0740.2470.5950.685
DRAFT0.0590.2410.9120.7140.9941.0000.0800.7510.1710.2980.1190.3070.1270.0890.9450.7040.574
BULD_YR-0.0620.2230.0370.0290.0910.0801.0000.0310.248-0.0500.190-0.032-0.0240.8640.0000.4490.735
DDWGHT0.5350.3580.9010.9840.7650.7510.0311.0000.1550.2550.0640.3380.218-0.1810.1950.5400.615
DPTRP_LA-0.1040.0460.1680.1560.1790.1710.2480.1551.000-0.1330.4490.024-0.0380.1670.6380.2760.417
DPTRP_LO0.0970.1300.3150.2690.3110.298-0.0500.255-0.1331.0000.0400.3140.099-0.1020.1770.1520.105
DTNT_LA-0.135-0.0630.1100.0760.1300.1190.1900.0640.4490.0401.000-0.098-0.0070.1720.4030.2530.397
DTNT_LO0.1980.0170.3510.3260.2990.307-0.0320.3380.0240.314-0.0981.000-0.147-0.0470.0000.1470.344
FRGHT_CNVNC_QTY0.1440.0300.1490.2370.1270.127-0.0240.218-0.0380.099-0.007-0.1471.000-0.0580.3130.5370.189
RN-0.309-0.004-0.107-0.2290.0740.0890.864-0.1810.167-0.1020.172-0.047-0.0581.0000.0000.4570.436
SHIP_KIND0.0000.0000.6840.6720.2470.9450.0000.1950.6380.1770.4030.0000.3130.0001.0000.7760.672
SHIP_OWNER_NM0.8160.5360.6500.7440.5950.7040.4490.5400.2760.1520.2530.1470.5370.4570.7761.0000.755
SHPYRD_NM0.6720.7860.8160.7490.6850.5740.7350.6150.4170.1050.3970.3440.1890.4360.6720.7551.000

Missing values

2023-12-10T23:28:37.878646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:28:38.083318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOFRGHT_CNVNC_QTYRN
05533823332578077FkduorwwhPdhuvnContainer42.8331.524.1Pdhuvn14.5RghqvhOlqgr200210965701-Jan-2023 00:00:0230-Apr-2023 23:56:21-26.7813-95.1176998.81153-79.53541473050002
15533943332578089FruqholdPdhuvnContainer42.8331.524.1Pdhuvn14.5RghqvhOlqgr200210460001-Jan-2023 00:05:3030-Apr-2023 23:59:5634.068501128.67999340.006302-61.8182982897780003
25534523332578091FroxpelqhPdhuvnContainer42.8331.524.1Pdhuvn14.5RghqvhOlqgr200210460001-Jan-2023 00:00:4730-Apr-2023 23:59:5722.294537.65969822.3323114.1169972172080004
35534973332578003FohphqwlqhPdhuvnContainer42.8331.524.1Pdhuvn14.5RghqvhOlqgr200210460001-Jan-2023 00:00:5031-Jan-2023 23:49:3430.478901122.28800232.2076142.199005237422005
45534103332593742DahoPdhuvnContainer42.8336.424.1Pdhuvn14.5RghqvhOlqgr200310900001-Jan-2023 00:01:3530-Apr-2023 23:59:2639.9823-65.00050425.56010135.5971981533330006
55534213332584947RojdPdhuvnContainer32.2224.818.2Pdhuvn12.0Yronvzhuiw20034102801-Jan-2023 00:01:4330-Apr-2023 23:59:3625.248301-73.516701-37.698299132.7400053473970007
65534223332593754DqqdPdhuvnContainer42.8336.424.1Pdhuvn14.5RghqvhOlqgr200310900001-Jan-2023 00:00:1530-Apr-2023 23:58:3852.657799-130.51600654.285-130.3600012844560008
75535393332593766DuqrogPdhuvnContainer42.8336.424.1Pdhuvn14.5RghqvhOlqgr200310900001-Jan-2023 00:05:4730-Apr-2023 23:45:4725.953335.29499826.530001-78.7632982437550009
85535303332584959RoxiPdhuvnContainer32.2224.018.5Pdhuvn12.0Yronvzhuiw20034102801-Jan-2023 00:04:3830-Apr-2023 23:57:588.82977-79.521599-32.495098-162.90699827337400010
95535493332584961RolyldPdhuvnContainer32.2224.818.5Pdhuvn12.0Yronvzhuiw20034102801-Jan-2023 00:01:0330-Apr-2023 23:11:24-37.973701144.87199424.4055-74.87840335415900011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOFRGHT_CNVNC_QTYRN
395516752332522133FPDFJPPhghdContainer42.8328.927.3FPDFJP16.0KbxqgdlVdpkrKL200611396401-Jan-2023 00:27:4930-Apr-2023 23:11:2230.493299124.95500230.622999122.05999816075400041
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425516879332522987FPDFJPUljrohwwrContainer42.8333.427.3FPDFJP16.0KbxqgdlKLXovdq200611396401-Jan-2023 00:01:0330-Apr-2023 23:59:3651.981998-160.41700748.516602-126.44599929617200044
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465516912332783969FPDFJPWlwxvContainer42.8319.024.6FPDFJP15.0VdpvxqjKL201110161201-Jan-2023 00:04:1730-Apr-2023 23:45:5821.45539.15829835.00999823.306717339100048
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485516069332132184FPDFJPIruwIohxuGHshhContainer(add)36.0210.018.3311.0FRVFRKLCkrxvkdq20193884001-Jan-2023 00:00:3130-Apr-2023 23:54:3550.0112-2.6073848.323299-7.7616725592200050