Overview

Dataset statistics

Number of variables25
Number of observations49
Missing cells24
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory221.7 B

Variable types

Numeric17
Text2
Categorical4
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001092

Alerts

PRT_NCHRG_TM has constant value ""Constant
NNVGTN_TM has constant value ""Constant
SHIP_OWNER_NM has 24 (49.0%) 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
PRCUSE_RT has unique valuesUnique
NVGTN_TM has unique valuesUnique
BLLAT_HOUR has unique valuesUnique
FRGHT_CNVNC_TM has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:19:54.269632
Analysis finished2023-12-10 14:19:55.010303
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MMSI
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2927306 × 108
Minimum2.29164 × 108
Maximum2.29396 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:19:55.114560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.29164 × 108
5-th percentile2.291774 × 108
Q12.29213 × 108
median2.29269 × 108
Q32.29342 × 108
95-th percentile2.29385 × 108
Maximum2.29396 × 108
Range232000
Interquartile range (IQR)129000

Descriptive statistics

Standard deviation70655.976
Coefficient of variation (CV)0.00030817391
Kurtosis-1.1446749
Mean2.2927306 × 108
Median Absolute Deviation (MAD)57000
Skewness0.33851901
Sum1.123438 × 1010
Variance4.992267 × 109
MonotonicityStrictly increasing
2023-12-10T23:19:55.285044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
229164000 1
 
2.0%
229347000 1
 
2.0%
229281000 1
 
2.0%
229283000 1
 
2.0%
229284000 1
 
2.0%
229285000 1
 
2.0%
229286000 1
 
2.0%
229287000 1
 
2.0%
229293000 1
 
2.0%
229305000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
229164000 1
2.0%
229166000 1
2.0%
229177000 1
2.0%
229178000 1
2.0%
229199000 1
2.0%
229200000 1
2.0%
229201000 1
2.0%
229202000 1
2.0%
229203000 1
2.0%
229204000 1
2.0%
ValueCountFrequency (%)
229396000 1
2.0%
229395000 1
2.0%
229387000 1
2.0%
229382000 1
2.0%
229381000 1
2.0%
229380000 1
2.0%
229376000 1
2.0%
229373000 1
2.0%
229364000 1
2.0%
229361000 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9557794.7
Minimum9153056
Maximum9662409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:19:55.480829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9153056
5-th percentile9284154.8
Q19490868
median9608702
Q39644196
95-th percentile9654315
Maximum9662409
Range509353
Interquartile range (IQR)153328

Descriptive statistics

Standard deviation124612.62
Coefficient of variation (CV)0.0130378
Kurtosis2.8495541
Mean9557794.7
Median Absolute Deviation (MAD)35846
Skewness-1.817882
Sum4.6833194 × 108
Variance1.5528304 × 1010
MonotonicityNot monotonic
2023-12-10T23:19:55.642273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9625449 1
 
2.0%
9512331 1
 
2.0%
9595981 1
 
2.0%
9609122 1
 
2.0%
9633408 1
 
2.0%
9633410 1
 
2.0%
9464651 1
 
2.0%
9585601 1
 
2.0%
9471630 1
 
2.0%
9630664 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9153056 1
2.0%
9187370 1
2.0%
9266140 1
2.0%
9311177 1
2.0%
9315537 1
2.0%
9455686 1
2.0%
9457854 1
2.0%
9464651 1
2.0%
9471630 1
2.0%
9476460 1
2.0%
ValueCountFrequency (%)
9662409 1
2.0%
9657789 1
2.0%
9657777 1
2.0%
9649122 1
2.0%
9649110 1
2.0%
9649108 1
2.0%
9649093 1
2.0%
9649081 1
2.0%
9649079 1
2.0%
9644548 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:19:55.931938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length9.4693878
Min length4

Characters and Unicode

Total characters464
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 rowERHAN
2nd rowAngel II
3rd rowTahiti One
4th rowBali
5th rowDENSA CHEETAH
ValueCountFrequency (%)
densa 6
 
7.8%
flag 2
 
2.6%
js 2
 
2.6%
lbc 2
 
2.6%
nba 2
 
2.6%
erhan 1
 
1.3%
green 1
 
1.3%
fiji 1
 
1.3%
pioneer 1
 
1.3%
minoan 1
 
1.3%
Other values (58) 58
75.3%
2023-12-10T23:19:56.419828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 36
 
7.8%
28
 
6.0%
a 25
 
5.4%
E 24
 
5.2%
i 23
 
5.0%
N 22
 
4.7%
n 21
 
4.5%
S 21
 
4.5%
e 19
 
4.1%
L 17
 
3.7%
Other values (37) 228
49.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 256
55.2%
Lowercase Letter 180
38.8%
Space Separator 28
 
6.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 36
14.1%
E 24
 
9.4%
N 22
 
8.6%
S 21
 
8.2%
L 17
 
6.6%
I 15
 
5.9%
B 13
 
5.1%
O 13
 
5.1%
M 12
 
4.7%
R 12
 
4.7%
Other values (14) 71
27.7%
Lowercase Letter
ValueCountFrequency (%)
a 25
13.9%
i 23
12.8%
n 21
11.7%
e 19
10.6%
o 16
8.9%
t 11
 
6.1%
r 9
 
5.0%
l 9
 
5.0%
g 7
 
3.9%
h 7
 
3.9%
Other values (12) 33
18.3%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 436
94.0%
Common 28
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 36
 
8.3%
a 25
 
5.7%
E 24
 
5.5%
i 23
 
5.3%
N 22
 
5.0%
n 21
 
4.8%
S 21
 
4.8%
e 19
 
4.4%
L 17
 
3.9%
o 16
 
3.7%
Other values (36) 212
48.6%
Common
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 36
 
7.8%
28
 
6.0%
a 25
 
5.4%
E 24
 
5.2%
i 23
 
5.0%
N 22
 
4.7%
n 21
 
4.5%
S 21
 
4.5%
e 19
 
4.1%
L 17
 
3.7%
Other values (37) 228
49.1%

SHIP_KIND
Categorical

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

2023-12-10T23:19:56.592265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:56.725142image/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)

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.264898
Minimum23.5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:19:56.855525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile27.8
Q130
median32.26
Q338
95-th percentile45
Maximum50
Range26.5
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.5345483
Coefficient of variation (CV)0.19070678
Kurtosis-0.024347116
Mean34.264898
Median Absolute Deviation (MAD)3.86
Skewness0.97748821
Sum1678.98
Variance42.700321
MonotonicityNot monotonic
2023-12-10T23:19:57.013670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
32.26 13
26.5%
45.0 6
12.2%
27.8 6
12.2%
30.0 4
 
8.2%
38.0 3
 
6.1%
32.25 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 (4) 7
14.3%
ValueCountFrequency (%)
23.5 1
 
2.0%
27.8 6
12.2%
28.3 2
 
4.1%
28.4 2
 
4.1%
30.0 4
 
8.2%
32.0 2
 
4.1%
32.2 2
 
4.1%
32.25 2
 
4.1%
32.26 13
26.5%
36.5 2
 
4.1%
ValueCountFrequency (%)
50.0 2
 
4.1%
45.0 6
12.2%
43.0 2
 
4.1%
38.0 3
 
6.1%
36.5 2
 
4.1%
32.26 13
26.5%
32.25 2
 
4.1%
32.2 2
 
4.1%
32.0 2
 
4.1%
30.0 4
 
8.2%

SHIP_LNTH
Real number (ℝ)

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.10612
Minimum168.5
Maximum294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:19:57.160439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168.5
5-th percentile172
Q1178
median205
Q3225.5
95-th percentile286.012
Maximum294
Range125.5
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation38.765524
Coefficient of variation (CV)0.18190713
Kurtosis-0.38836346
Mean213.10612
Median Absolute Deviation (MAD)22
Skewness0.85326057
Sum10442.2
Variance1502.7659
MonotonicityNot monotonic
2023-12-10T23:19:57.302291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
178.0 6
 
12.2%
172.0 4
 
8.2%
222.0 4
 
8.2%
193.74 4
 
8.2%
225.5 4
 
8.2%
282.0 3
 
6.1%
217.0 3
 
6.1%
294.0 2
 
4.1%
185.0 2
 
4.1%
183.0 2
 
4.1%
Other values (13) 15
30.6%
ValueCountFrequency (%)
168.5 1
 
2.0%
171.5 1
 
2.0%
172.0 4
8.2%
176.0 1
 
2.0%
178.0 6
12.2%
181.0 1
 
2.0%
183.0 2
 
4.1%
185.0 2
 
4.1%
185.34 1
 
2.0%
193.74 4
8.2%
ValueCountFrequency (%)
294.0 2
4.1%
287.9 1
 
2.0%
283.18 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%
223.0 2
4.1%
222.0 4
8.2%

SHIP_HGHT
Real number (ℝ)

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.836735
Minimum14.1
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:19:57.468960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.1
5-th percentile14.43
Q115.6
median18.5
Q320.1
95-th percentile24.8
Maximum24.9
Range10.8
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.3593467
Coefficient of variation (CV)0.17834018
Kurtosis-0.65923766
Mean18.836735
Median Absolute Deviation (MAD)2.2
Skewness0.46124328
Sum923
Variance11.28521
MonotonicityNot monotonic
2023-12-10T23:19:57.601877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
15.6 6
 
12.2%
20.05 5
 
10.2%
14.7 4
 
8.2%
18.5 4
 
8.2%
20.7 3
 
6.1%
24.8 3
 
6.1%
18.0 3
 
6.1%
20.1 2
 
4.1%
14.1 2
 
4.1%
17.7 2
 
4.1%
Other values (13) 15
30.6%
ValueCountFrequency (%)
14.1 2
 
4.1%
14.25 1
 
2.0%
14.7 4
8.2%
15.2 1
 
2.0%
15.21 1
 
2.0%
15.6 6
12.2%
16.5 1
 
2.0%
17.7 2
 
4.1%
18.0 3
6.1%
18.5 4
8.2%
ValueCountFrequency (%)
24.9 2
 
4.1%
24.8 3
6.1%
24.75 2
 
4.1%
24.5 1
 
2.0%
20.7 3
6.1%
20.2 1
 
2.0%
20.1 2
 
4.1%
20.05 5
10.2%
19.9 1
 
2.0%
19.7 1
 
2.0%

SHIP_OWNER_NM
Text

MISSING 

Distinct13
Distinct (%)52.0%
Missing24
Missing (%)49.0%
Memory size524.0 B
2023-12-10T23:19:57.827519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length13.08
Min length8

Characters and Unicode

Total characters327
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

Unique10 ?
Unique (%)40.0%

Sample

1st rowSea Traders
2nd rowSea Traders
3rd rowSea Traders
4th rowUnimar Success
5th rowMinerva Marine
ValueCountFrequency (%)
sea 9
16.7%
traders 9
16.7%
nyk 4
 
7.4%
blkshp 4
 
7.4%
atlnt 4
 
7.4%
marine 3
 
5.6%
minerva 2
 
3.7%
sa 2
 
3.7%
hellenic 1
 
1.9%
enesel 1
 
1.9%
Other values (15) 15
27.8%
2023-12-10T23:19:58.275692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 31
 
9.5%
r 30
 
9.2%
a 29
 
8.9%
29
 
8.9%
s 21
 
6.4%
i 18
 
5.5%
n 17
 
5.2%
l 16
 
4.9%
d 13
 
4.0%
S 12
 
3.7%
Other values (28) 111
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 233
71.3%
Uppercase Letter 64
 
19.6%
Space Separator 29
 
8.9%
Other Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31
13.3%
r 30
12.9%
a 29
12.4%
s 21
9.0%
i 18
7.7%
n 17
7.3%
l 16
6.9%
d 13
 
5.6%
t 11
 
4.7%
o 10
 
4.3%
Other values (9) 37
15.9%
Uppercase Letter
ValueCountFrequency (%)
S 12
18.8%
T 9
14.1%
A 7
10.9%
M 7
10.9%
B 5
7.8%
N 4
 
6.2%
Y 4
 
6.2%
K 4
 
6.2%
U 2
 
3.1%
C 2
 
3.1%
Other values (7) 8
12.5%
Space Separator
ValueCountFrequency (%)
29
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 297
90.8%
Common 30
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31
 
10.4%
r 30
 
10.1%
a 29
 
9.8%
s 21
 
7.1%
i 18
 
6.1%
n 17
 
5.7%
l 16
 
5.4%
d 13
 
4.4%
S 12
 
4.0%
t 11
 
3.7%
Other values (26) 99
33.3%
Common
ValueCountFrequency (%)
29
96.7%
. 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31
 
9.5%
r 30
 
9.2%
a 29
 
8.9%
29
 
8.9%
s 21
 
6.4%
i 18
 
5.5%
n 17
 
5.2%
l 16
 
4.9%
d 13
 
4.0%
S 12
 
3.7%
Other values (28) 111
33.9%

DRAFT
Real number (ℝ)

Distinct22
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.620004
Minimum3
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:19:58.448676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q117.6442
median28.8672
Q330
95-th percentile30
Maximum30
Range27
Interquartile range (IQR)12.3558

Descriptive statistics

Standard deviation9.0221634
Coefficient of variation (CV)0.38197129
Kurtosis0.29533427
Mean23.620004
Median Absolute Deviation (MAD)1.1328
Skewness-1.2938794
Sum1157.3802
Variance81.399432
MonotonicityNot monotonic
2023-12-10T23:19:58.599644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
30.0 20
40.8%
5.0 5
 
10.2%
27.2114 4
 
8.2%
29.5231 2
 
4.1%
18.1539 1
 
2.0%
4.0 1
 
2.0%
18.1537 1
 
2.0%
17.6442 1
 
2.0%
24.3378 1
 
2.0%
17.5141 1
 
2.0%
Other values (12) 12
24.5%
ValueCountFrequency (%)
3.0 1
 
2.0%
4.0 1
 
2.0%
5.0 5
10.2%
17.49 1
 
2.0%
17.4915 1
 
2.0%
17.5141 1
 
2.0%
17.5187 1
 
2.0%
17.5503 1
 
2.0%
17.6442 1
 
2.0%
18.1537 1
 
2.0%
ValueCountFrequency (%)
30.0 20
40.8%
29.5231 2
 
4.1%
29.0691 1
 
2.0%
28.8774 1
 
2.0%
28.8672 1
 
2.0%
28.8625 1
 
2.0%
28.8437 1
 
2.0%
27.2114 4
 
8.2%
25.2942 1
 
2.0%
24.3378 1
 
2.0%

SHPYRD_NM
Categorical

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
<NA>
24 
New Century SB
5 
SCS Shipbuilding
4 
Jiangsu New YZJ
3 
Oshima Shipbuilding
3 
Other values (7)
10 

Length

Max length19
Median length18
Mean length10
Min length4

Unique

Unique4 ?
Unique (%)8.2%

Sample

1st row<NA>
2nd rowNew Times SB
3rd rowHyundai HI (Ulsan)
4th rowHyundai HI (Ulsan)
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 24
49.0%
New Century SB 5
 
10.2%
SCS Shipbuilding 4
 
8.2%
Jiangsu New YZJ 3
 
6.1%
Oshima Shipbuilding 3
 
6.1%
New Times SB 2
 
4.1%
Hyundai HI (Ulsan) 2
 
4.1%
Tsuneishi Zosen 2
 
4.1%
Shanghai Waigaoqiao 1
 
2.0%
Hudong Zhonghua 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2023-12-10T23:19:58.742020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 24
27.3%
new 10
11.4%
sb 8
 
9.1%
shipbuilding 7
 
8.0%
century 5
 
5.7%
scs 4
 
4.5%
jiangsu 3
 
3.4%
yzj 3
 
3.4%
oshima 3
 
3.4%
zosen 2
 
2.3%
Other values (14) 19
21.6%

BULD_YR
Real number (ℝ)

Distinct10
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.449
Minimum1998
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:19:58.876304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile2003.8
Q12012
median2013
Q32013
95-th percentile2013
Maximum2014
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.4824634
Coefficient of variation (CV)0.0017313207
Kurtosis8.5047544
Mean2011.449
Median Absolute Deviation (MAD)0
Skewness-2.9398753
Sum98561
Variance12.127551
MonotonicityNot monotonic
2023-12-10T23:19:59.031524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2013 29
59.2%
2012 8
 
16.3%
2011 4
 
8.2%
1998 2
 
4.1%
2009 1
 
2.0%
2005 1
 
2.0%
2014 1
 
2.0%
2007 1
 
2.0%
2003 1
 
2.0%
2010 1
 
2.0%
ValueCountFrequency (%)
1998 2
 
4.1%
2003 1
 
2.0%
2005 1
 
2.0%
2007 1
 
2.0%
2009 1
 
2.0%
2010 1
 
2.0%
2011 4
 
8.2%
2012 8
 
16.3%
2013 29
59.2%
2014 1
 
2.0%
ValueCountFrequency (%)
2014 1
 
2.0%
2013 29
59.2%
2012 8
 
16.3%
2011 4
 
8.2%
2010 1
 
2.0%
2009 1
 
2.0%
2007 1
 
2.0%
2005 1
 
2.0%
2003 1
 
2.0%
1998 2
 
4.1%

DDWGHT
Real number (ℝ)

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81465.245
Minimum27780
Maximum206046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:19:59.213411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27780
5-th percentile33486.6
Q137009
median70663
Q393207
95-th percentile180557.4
Maximum206046
Range178266
Interquartile range (IQR)56198

Descriptive statistics

Standard deviation51006.991
Coefficient of variation (CV)0.62611965
Kurtosis0.50654533
Mean81465.245
Median Absolute Deviation (MAD)33076
Skewness1.2448034
Sum3991797
Variance2.6017131 × 109
MonotonicityNot monotonic
2023-12-10T23:19:59.376713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63200 4
 
8.2%
58000 2
 
4.1%
37587 2
 
4.1%
35176 1
 
2.0%
82099 1
 
2.0%
81386 1
 
2.0%
114563 1
 
2.0%
175125 1
 
2.0%
93283 1
 
2.0%
81285 1
 
2.0%
Other values (34) 34
69.4%
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%
35176 1
2.0%
36722 1
2.0%
36746 1
2.0%
36752 1
2.0%
ValueCountFrequency (%)
206046 1
2.0%
206037 1
2.0%
182307 1
2.0%
177933 1
2.0%
176460 1
2.0%
176247 1
2.0%
175191 1
2.0%
175125 1
2.0%
114563 1
2.0%
107236 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
2023-12-10T23:19:59.535167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:59.756569image/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
2023-12-10T23:20:00.004874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:00.198914image/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%
Mean12.098818
Minimum-38.784401
Maximum57.7784
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)30.6%
Memory size573.0 B
2023-12-10T23:20:00.409174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-38.784401
5-th percentile-34.49122
Q1-7.31044
median15.1533
Q331.241501
95-th percentile51.720019
Maximum57.7784
Range96.562801
Interquartile range (IQR)38.551941

Descriptive statistics

Standard deviation28.490873
Coefficient of variation (CV)2.3548477
Kurtosis-0.94991463
Mean12.098818
Median Absolute Deviation (MAD)18.9008
Skewness-0.37363954
Sum592.84208
Variance811.72987
MonotonicityNot monotonic
2023-12-10T23:20:00.890120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
36.7234 1
 
2.0%
36.248299 1
 
2.0%
-38.784401 1
 
2.0%
6.9564 1
 
2.0%
43.891701 1
 
2.0%
15.1533 1
 
2.0%
50.9963 1
 
2.0%
3.81615 1
 
2.0%
29.8622 1
 
2.0%
-36.190601 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-38.784401 1
2.0%
-36.190601 1
2.0%
-34.741501 1
2.0%
-34.115799 1
2.0%
-33.964401 1
2.0%
-33.926701 1
2.0%
-33.0098 1
2.0%
-30.094801 1
2.0%
-28.9121 1
2.0%
-20.087601 1
2.0%
ValueCountFrequency (%)
57.7784 1
2.0%
56.5882 1
2.0%
52.202499 1
2.0%
50.9963 1
2.0%
46.937901 1
2.0%
46.292702 1
2.0%
45.6371 1
2.0%
43.891701 1
2.0%
36.7234 1
2.0%
36.248299 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.106794
Minimum-150.99699
Maximum153.49699
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)34.7%
Memory size573.0 B
2023-12-10T23:20:01.076074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-150.99699
5-th percentile-114.09832
Q1-62.301102
median29.563601
Q3100.582
95-th percentile121.872
Maximum153.49699
Range304.49399
Interquartile range (IQR)162.8831

Descriptive statistics

Standard deviation83.612148
Coefficient of variation (CV)5.1911105
Kurtosis-1.113526
Mean16.106794
Median Absolute Deviation (MAD)83.122396
Skewness-0.22570883
Sum789.23291
Variance6990.9913
MonotonicityNot monotonic
2023-12-10T23:20:01.232672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5.16162 1
 
2.0%
-2.415 1
 
2.0%
-62.301102 1
 
2.0%
79.854599 1
 
2.0%
29.563601 1
 
2.0%
-129.341995 1
 
2.0%
1.32422 1
 
2.0%
105.745003 1
 
2.0%
49.200199 1
 
2.0%
52.959599 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%
-94.647003 1
2.0%
-90.506302 1
2.0%
-89.983498 1
2.0%
-89.866302 1
2.0%
-79.428703 1
2.0%
-73.481201 1
2.0%
ValueCountFrequency (%)
153.496994 1
2.0%
135.139999 1
2.0%
122.031998 1
2.0%
121.632004 1
2.0%
120.227997 1
2.0%
118.633003 1
2.0%
115.720001 1
2.0%
114.434998 1
2.0%
113.658997 1
2.0%
112.685997 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3519834
Minimum-35.942799
Maximum59.7024
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:20:01.430574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-35.942799
5-th percentile-34.078879
Q1-20.579201
median12.5202
Q333.123299
95-th percentile47.498421
Maximum59.7024
Range95.645199
Interquartile range (IQR)53.7025

Descriptive statistics

Standard deviation28.131889
Coefficient of variation (CV)3.0081201
Kurtosis-1.2084729
Mean9.3519834
Median Absolute Deviation (MAD)24.161101
Skewness-0.13374301
Sum458.24719
Variance791.40317
MonotonicityNot monotonic
2023-12-10T23:20:01.647396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-33.730999 1
 
2.0%
-11.6751 1
 
2.0%
59.7024 1
 
2.0%
-23.846701 1
 
2.0%
-35.942799 1
 
2.0%
-21.178301 1
 
2.0%
-20.745001 1
 
2.0%
-14.5706 1
 
2.0%
12.5202 1
 
2.0%
-5.995 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%
-33.730999 1
2.0%
-32.696201 1
2.0%
-28.962299 1
2.0%
-27.508699 1
2.0%
-25.639999 1
2.0%
-23.976101 1
2.0%
-23.846701 1
2.0%
ValueCountFrequency (%)
59.7024 1
2.0%
57.3703 1
2.0%
48.4459 1
2.0%
46.077202 1
2.0%
45.0285 1
2.0%
41.5284 1
2.0%
38.6982 1
2.0%
36.9907 1
2.0%
36.818298 1
2.0%
36.681301 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.241723
Minimum-152.45799
Maximum151.46001
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:20:01.812305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-152.45799
5-th percentile-90.024939
Q1-39.646702
median31.9053
Q3103.939
95-th percentile135.003
Maximum151.46001
Range303.918
Interquartile range (IQR)143.5857

Descriptive statistics

Standard deviation77.355821
Coefficient of variation (CV)2.8396082
Kurtosis-0.88351651
Mean27.241723
Median Absolute Deviation (MAD)72.033703
Skewness-0.20506673
Sum1334.8444
Variance5983.923
MonotonicityNot monotonic
2023-12-10T23:20:01.966553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-59.361401 1
 
2.0%
-35.895302 1
 
2.0%
23.844801 1
 
2.0%
151.460007 1
 
2.0%
12.0002 1
 
2.0%
149.348007 1
 
2.0%
116.224998 1
 
2.0%
117.078003 1
 
2.0%
47.181099 1
 
2.0%
79.550003 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-152.457993 1
2.0%
-91.505203 1
2.0%
-90.583298 1
2.0%
-89.187401 1
2.0%
-86.834602 1
2.0%
-77.074997 1
2.0%
-60.7229 1
2.0%
-59.361401 1
2.0%
-56.041 1
2.0%
-51.970299 1
2.0%
ValueCountFrequency (%)
151.460007 1
2.0%
149.348007 1
2.0%
139.852997 1
2.0%
127.727997 1
2.0%
126.724998 1
2.0%
120.25 1
2.0%
117.199997 1
2.0%
117.078003 1
2.0%
116.224998 1
2.0%
112.883003 1
2.0%

PRCUSE_RT
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95164635
Minimum0.76643
Maximum0.999787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:20:02.143744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.76643
5-th percentile0.837182
Q10.93511
median0.973897
Q30.989016
95-th percentile0.9989296
Maximum0.999787
Range0.233357
Interquartile range (IQR)0.053906

Descriptive statistics

Standard deviation0.053832894
Coefficient of variation (CV)0.056568171
Kurtosis2.8381775
Mean0.95164635
Median Absolute Deviation (MAD)0.018322
Skewness-1.7613502
Sum46.630671
Variance0.0028979804
MonotonicityNot monotonic
2023-12-10T23:20:02.291801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.907617 1
 
2.0%
0.997909 1
 
2.0%
0.989016 1
 
2.0%
0.978734 1
 
2.0%
0.992219 1
 
2.0%
0.879052 1
 
2.0%
0.978013 1
 
2.0%
0.968922 1
 
2.0%
0.947121 1
 
2.0%
0.935935 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.76643 1
2.0%
0.80364 1
2.0%
0.830714 1
2.0%
0.846884 1
2.0%
0.864369 1
2.0%
0.879052 1
2.0%
0.90271 1
2.0%
0.907617 1
2.0%
0.907804 1
2.0%
0.910089 1
2.0%
ValueCountFrequency (%)
0.999787 1
2.0%
0.999782 1
2.0%
0.99961 1
2.0%
0.997909 1
2.0%
0.996006 1
2.0%
0.994594 1
2.0%
0.992674 1
2.0%
0.992614 1
2.0%
0.992219 1
2.0%
0.991195 1
2.0%

NVGTN_TM
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4600.0771
Minimum3594.52
Maximum4725.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:20:02.469871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3594.52
5-th percentile3715.114
Q14694.92
median4724.3
Q34725.56
95-th percentile4725.922
Maximum4725.98
Range1131.46
Interquartile range (IQR)30.64

Descriptive statistics

Standard deviation295.40878
Coefficient of variation (CV)0.064218222
Kurtosis5.9087471
Mean4600.0771
Median Absolute Deviation (MAD)1.63
Skewness-2.6471002
Sum225403.78
Variance87266.345
MonotonicityNot monotonic
2023-12-10T23:20:02.613429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4696.08 1
 
2.0%
4724.53 1
 
2.0%
4725.78 1
 
2.0%
4725.87 1
 
2.0%
4725.55 1
 
2.0%
4720.12 1
 
2.0%
4724.62 1
 
2.0%
4376.08 1
 
2.0%
3712.47 1
 
2.0%
4725.98 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3594.52 1
2.0%
3700.79 1
2.0%
3712.47 1
2.0%
3719.08 1
2.0%
4198.38 1
2.0%
4376.08 1
2.0%
4430.29 1
2.0%
4501.29 1
2.0%
4567.53 1
2.0%
4594.44 1
2.0%
ValueCountFrequency (%)
4725.98 1
2.0%
4725.97 1
2.0%
4725.93 1
2.0%
4725.91 1
2.0%
4725.88 1
2.0%
4725.87 1
2.0%
4725.86 1
2.0%
4725.84 1
2.0%
4725.83 1
2.0%
4725.82 1
2.0%

PRT_NCHRG_TM
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
100.0%

Length

2023-12-10T23:20:02.766892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:02.892162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

BLLAT_HOUR
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.6128
Minimum0.963611
Maximum1103.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:20:03.025654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.963611
5-th percentile4.99028
Q151.9142
median113.932
Q3306.657
95-th percentile735.6094
Maximum1103.72
Range1102.7564
Interquartile range (IQR)254.7428

Descriptive statistics

Standard deviation247.95932
Coefficient of variation (CV)1.1239571
Kurtosis3.4312855
Mean220.6128
Median Absolute Deviation (MAD)81.319
Skewness1.8669047
Sum10810.027
Variance61483.825
MonotonicityNot monotonic
2023-12-10T23:20:03.176662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
433.837 1
 
2.0%
9.88361 1
 
2.0%
51.9142 1
 
2.0%
100.507 1
 
2.0%
36.7647 1
 
2.0%
570.883 1
 
2.0%
103.882 1
 
2.0%
136.0 1
 
2.0%
196.31 1
 
2.0%
302.775 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.963611 1
2.0%
1.03056 1
2.0%
1.72806 1
2.0%
9.88361 1
2.0%
18.87 1
2.0%
25.5489 1
2.0%
33.6622 1
2.0%
34.8942 1
2.0%
36.7647 1
2.0%
41.6019 1
2.0%
ValueCountFrequency (%)
1103.72 1
2.0%
927.979 1
2.0%
798.841 1
2.0%
640.762 1
2.0%
570.883 1
2.0%
566.649 1
2.0%
459.515 1
2.0%
433.837 1
2.0%
424.893 1
2.0%
373.268 1
2.0%

FRGHT_CNVNC_TM
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4379.4641
Minimum3134.14
Maximum4723.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:20:03.318315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3134.14
5-th percentile3552.38
Q14263.61
median4516.22
Q34634.59
95-th percentile4703.91
Maximum4723.68
Range1589.54
Interquartile range (IQR)370.98

Descriptive statistics

Standard deviation390.70217
Coefficient of variation (CV)0.089212323
Kurtosis2.3924716
Mean4379.4641
Median Absolute Deviation (MAD)149.93
Skewness-1.725186
Sum214593.74
Variance152648.18
MonotonicityNot monotonic
2023-12-10T23:20:03.493302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4262.24 1
 
2.0%
4714.65 1
 
2.0%
4673.87 1
 
2.0%
4625.37 1
 
2.0%
4688.78 1
 
2.0%
4149.23 1
 
2.0%
4620.74 1
 
2.0%
4240.08 1
 
2.0%
3516.16 1
 
2.0%
4423.21 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3134.14 1
2.0%
3263.12 1
2.0%
3516.16 1
2.0%
3606.71 1
2.0%
3621.71 1
2.0%
3797.93 1
2.0%
3920.03 1
2.0%
4083.54 1
2.0%
4088.79 1
2.0%
4149.23 1
2.0%
ValueCountFrequency (%)
4723.68 1
2.0%
4714.65 1
2.0%
4706.31 1
2.0%
4700.31 1
2.0%
4690.5 1
2.0%
4688.78 1
2.0%
4682.95 1
2.0%
4676.6 1
2.0%
4673.87 1
2.0%
4666.15 1
2.0%

NNVGTN_TM
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
100.0%

Length

2023-12-10T23:20:03.673939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:03.763305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

RN
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:20:03.895270image/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
2023-12-10T23:20:04.075454image/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%

Sample

MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOPRCUSE_RTNVGTN_TMPRT_NCHRG_TMBLLAT_HOURFRGHT_CNVNC_TMNNVGTN_TMRN
02291640009625449ERHANBULK CARRIER30.0172.014.7<NA>5.0<NA>20133517602-Jan-2022 05:54:1617-Jul-2022 21:58:5336.72345.16162-33.730999-59.3614010.9076174696.080433.8374262.2402
12291660009511349Angel IIBulk Carrier45.0283.1824.75Sea Traders17.4915New Times SB201217519110-Jan-2022 06:39:4117-Jul-2022 19:57:0631.241501122.031998-20.579201117.1999970.9997874501.2900.9636114500.3303
22291770009597032Tahiti OneBulk Carrier32.25223.020.1Sea Traders30.0Hyundai HI (Ulsan)20128129201-Jan-2022 00:07:2617-Jul-2022 21:58:0022.558901120.227997-2.12167104.9779970.9789274725.84099.58974626.2504
32291780009597020BaliBulk Carrier32.25223.020.1Sea Traders30.0Hyundai HI (Ulsan)20128125901-Jan-2022 00:07:0317-Jul-2022 21:56:4529.608999-89.866302-27.50869944.57040.935114725.830306.6574419.1705
42291990009649122DENSA CHEETAHBULK CARRIER27.8178.015.6<NA>29.5231<NA>20133758701-Jan-2022 04:17:5917-Jul-2022 05:23:3029.233101-94.64700333.123299-8.631670.9585024705.090195.2514509.8406
52292000009649110DENSA SEALBULK CARRIER27.8178.015.6<NA>29.5231<NA>20133758701-Jan-2022 01:07:4417-Jul-2022 21:50:2357.77849.837533.89357-77.0749970.9997824724.7101.030564723.6807
62292010009649108DENSA PUMABULK CARRIER27.8178.015.6<NA>28.8437<NA>20133672201-Jan-2022 00:06:0914-Jul-2022 14:24:49-33.0098-71.592328.087601-86.8346020.9802344646.31091.84644554.4708
72292020009649093DENSA HAWKBULK CARRIER27.8178.015.6<NA>28.8625<NA>20133674601-Jan-2022 00:03:2617-Jul-2022 21:55:1352.2024994.2505846.077202-1.251960.9945944725.86025.54894700.3109
82292030009649081DENSA FALCONBULK CARRIER27.8178.015.6<NA>28.8672<NA>20133675201-Jan-2022 07:25:0917-Jul-2022 21:52:5828.0714135.13999938.6982-9.17330.9728984718.470127.8744590.59010
92292040009649079DENSA SEA LIONBULK CARRIER27.8178.015.6<NA>28.8774<NA>20133676501-Jan-2022 00:40:0717-Jul-2022 21:58:5710.1567-79.4287037.28333108.9580.9210084725.310373.2684352.05011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOPRCUSE_RTNVGTN_TMPRT_NCHRG_TMBLLAT_HOURFRGHT_CNVNC_TMNNVGTN_TMRN
392293610009662409SchinousaBulk Carrier45.0282.024.8Minerva Marine17.5141SCS Shipbuilding201417624701-Jan-2022 00:07:2717-Jul-2022 12:57:06-9.4755115.72000141.528431.90530.9892554716.83050.67284666.15041
402293640009153056SEAEAGLEBULK CARRIER32.2215.018.6<NA>30.0<NA>19987166301-Jan-2022 01:01:3217-Jul-2022 20:09:0111.929945.271919.078861.6460.902714723.120459.5154263.61042
412293730009616905Hampton BridgeBulk Carrier32.26217.019.9Rimorchiato. Riuniti30.0SPP Tongyoung SY20137684701-Jan-2022 00:26:1517-Jul-2022 22:00:04-34.741501-57.8058011.28721103.9390030.9758914725.560113.9324611.63043
422293760009643908FLAG GANGOSBULK CARRIER32.26185.018.0<NA>24.3378<NA>20135652601-Jan-2022 00:01:0417-Jul-2022 21:56:2922.451401-97.757797-34.310799-51.9702990.9770994725.930108.2224617.7044
432293800009311177Alpha LoyaltyBulk Carrier32.26217.019.3Alpha Bulkers30.0Tsuneishi Zosen20077588401-Jan-2022 00:06:4417-Jul-2022 21:17:1846.292702-124.991997-23.976101-46.2871020.9960064725.18018.874706.31045
442293810009266140FIRST BROTHERBULK CARRIER28.4168.514.25<NA>30.0<NA>20033238501-Jan-2022 00:04:2317-Jul-2022 21:33:5146.93790132.020599-32.696201-60.72290.9807644725.49090.90034634.59046
452293820009479204Thalassini AgathaBulk Carrier45.0287.924.5Enesel SA17.6442Universal SB (Tsu)201118230701-Jan-2022 00:06:4805-Jun-2022 23:11:31-10.056572.727203-28.96229932.1291010.9697863719.080112.3673606.71047
462293870009587257HuahineBulk Carrier50.0294.024.9Dryships18.1537SCS Shipbuilding201320603701-Jan-2022 00:04:2217-Jul-2022 21:46:37-33.926701113.658997-0.652322-8.468940.9100894725.70424.8934300.81048
472293950009490777JS BANDOLBULK CARRIER32.26185.018.0<NA>5.0<NA>20105800001-Jan-2022 15:18:2517-Jul-2022 13:56:0031.606431.743732.614399127.7279970.9584424702.630195.4234507.2049
482293960009490868JS POMEROLBULK CARRIER32.26185.3418.0<NA>4.0<NA>20115800001-Jan-2022 00:38:5917-Jul-2022 21:48:369.93235-61.61836.9907126.7249980.9859074725.16066.58674658.57050