Overview

Dataset statistics

Number of variables24
Number of observations49
Missing cells58
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory212.7 B

Variable types

Numeric16
Text3
Categorical3
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001089

Alerts

PRT_NCHRG_TM has constant value ""Constant
NNVGTN_TM has constant value ""Constant
SHIP_OWNER_NM has 29 (59.2%) missing valuesMissing
SHPYRD_NM has 29 (59.2%) missing valuesMissing
MMSI has unique valuesUnique
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM has unique valuesUnique
DPTR_HMS has unique valuesUnique
DPTRP_LA has unique valuesUnique
DPTRP_LO has unique valuesUnique
DTNT_LA has unique valuesUnique
DTNT_LO has unique valuesUnique
BLLAT_HOUR has unique valuesUnique
FRGHT_CNVNC_TM has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:32:05.221116
Analysis finished2023-12-10 14:32:05.514965
Duration0.29 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%
Mean2.2903216 × 108
Minimum2.280846 × 108
Maximum2.29215 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:05.583528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.280846 × 108
5-th percentile2.2836912 × 108
Q12.29027 × 108
median2.29126 × 108
Q32.29177 × 108
95-th percentile2.292126 × 108
Maximum2.29215 × 108
Range1130400
Interquartile range (IQR)150000

Descriptive statistics

Standard deviation268436.65
Coefficient of variation (CV)0.0011720478
Kurtosis5.311771
Mean2.2903216 × 108
Median Absolute Deviation (MAD)77000
Skewness-2.4803254
Sum1.1222576 × 1010
Variance7.2058233 × 1010
MonotonicityStrictly increasing
2023-12-10T23:32:05.748501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
228084600 1
 
2.0%
229178000 1
 
2.0%
229135000 1
 
2.0%
229141000 1
 
2.0%
229143000 1
 
2.0%
229146000 1
 
2.0%
229158000 1
 
2.0%
229159000 1
 
2.0%
229161000 1
 
2.0%
229164000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
228084600 1
2.0%
228200800 1
2.0%
228366800 1
2.0%
228372600 1
2.0%
228372900 1
2.0%
229003000 1
2.0%
229010000 1
2.0%
229011000 1
2.0%
229012000 1
2.0%
229013000 1
2.0%
ValueCountFrequency (%)
229215000 1
2.0%
229214000 1
2.0%
229213000 1
2.0%
229212000 1
2.0%
229207000 1
2.0%
229204000 1
2.0%
229203000 1
2.0%
229202000 1
2.0%
229201000 1
2.0%
229200000 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9579737.4
Minimum9103790
Maximum9837353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:05.933804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9103790
5-th percentile9427434.8
Q19512305
median9605839
Q39634713
95-th percentile9782594.8
Maximum9837353
Range733563
Interquartile range (IQR)122408

Descriptive statistics

Standard deviation130195.23
Coefficient of variation (CV)0.013590689
Kurtosis4.663598
Mean9579737.4
Median Absolute Deviation (MAD)43242
Skewness-1.3907573
Sum4.6940714 × 108
Variance1.6950798 × 1010
MonotonicityNot monotonic
2023-12-10T23:32:06.193958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9777541 1
 
2.0%
9597020 1
 
2.0%
9594561 1
 
2.0%
9468138 1
 
2.0%
9426104 1
 
2.0%
9598787 1
 
2.0%
9644172 1
 
2.0%
9644184 1
 
2.0%
9429431 1
 
2.0%
9625449 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9103790 1
2.0%
9163570 1
2.0%
9426104 1
2.0%
9429431 1
2.0%
9455686 1
2.0%
9461257 1
2.0%
9468138 1
2.0%
9476460 1
2.0%
9486477 1
2.0%
9498274 1
2.0%
ValueCountFrequency (%)
9837353 1
2.0%
9837341 1
2.0%
9785964 1
2.0%
9777541 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%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:32:06.631274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length9.6734694
Min length4

Characters and Unicode

Total characters474
Distinct characters44
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 rowMAYOURY
2nd rowCAPO NERO
3rd rowHYDRA
4th rowSEDNA
5th rowCAPO CINTO
ValueCountFrequency (%)
densa 6
 
7.9%
adfines 4
 
5.3%
iolcos 3
 
3.9%
capo 2
 
2.6%
mayoury 1
 
1.3%
kenan 1
 
1.3%
angel 1
 
1.3%
erhan 1
 
1.3%
harmony 1
 
1.3%
sinop 1
 
1.3%
Other values (55) 55
72.4%
2023-12-10T23:32:07.362038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 41
 
8.6%
N 30
 
6.3%
27
 
5.7%
E 26
 
5.5%
a 25
 
5.3%
S 22
 
4.6%
I 21
 
4.4%
O 20
 
4.2%
i 18
 
3.8%
D 18
 
3.8%
Other values (34) 226
47.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 280
59.1%
Lowercase Letter 167
35.2%
Space Separator 27
 
5.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 41
14.6%
N 30
10.7%
E 26
9.3%
S 22
 
7.9%
I 21
 
7.5%
O 20
 
7.1%
D 18
 
6.4%
T 16
 
5.7%
M 13
 
4.6%
R 12
 
4.3%
Other values (14) 61
21.8%
Lowercase Letter
ValueCountFrequency (%)
a 25
15.0%
i 18
10.8%
n 16
9.6%
o 16
9.6%
e 15
9.0%
r 12
 
7.2%
l 9
 
5.4%
s 9
 
5.4%
m 7
 
4.2%
t 7
 
4.2%
Other values (9) 33
19.8%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 447
94.3%
Common 27
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 41
 
9.2%
N 30
 
6.7%
E 26
 
5.8%
a 25
 
5.6%
S 22
 
4.9%
I 21
 
4.7%
O 20
 
4.5%
i 18
 
4.0%
D 18
 
4.0%
T 16
 
3.6%
Other values (33) 210
47.0%
Common
ValueCountFrequency (%)
27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 41
 
8.6%
N 30
 
6.3%
27
 
5.7%
E 26
 
5.5%
a 25
 
5.3%
S 22
 
4.6%
I 21
 
4.4%
O 20
 
4.2%
i 18
 
3.8%
D 18
 
3.8%
Other values (34) 226
47.7%

SHIP_KIND
Categorical

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
BULK CARRIER
24 
Bulk Carrier
20 
Chemical/Oil Product
3 
Cement Carrier
 
2

Length

Max length20
Median length12
Mean length12.571429
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChemical/Oil Product
2nd rowCement Carrier
3rd rowChemical/Oil Product
4th rowChemical/Oil Product
5th rowCement Carrier

Common Values

ValueCountFrequency (%)
BULK CARRIER 24
49.0%
Bulk Carrier 20
40.8%
Chemical/Oil Product 3
 
6.1%
Cement Carrier 2
 
4.1%

Length

2023-12-10T23:32:07.567942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:07.685305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
carrier 46
46.9%
bulk 44
44.9%
chemical/oil 3
 
3.1%
product 3
 
3.1%
cement 2
 
2.0%

SHIP_WDTH
Real number (ℝ)

Distinct16
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.514694
Minimum13.75
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:07.779565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.75
5-th percentile18
Q128.5
median32.25
Q332.26
95-th percentile45
Maximum50
Range36.25
Interquartile range (IQR)3.76

Descriptive statistics

Standard deviation7.3878724
Coefficient of variation (CV)0.23442628
Kurtosis0.99707818
Mean31.514694
Median Absolute Deviation (MAD)3.75
Skewness0.17137203
Sum1544.22
Variance54.580659
MonotonicityNot monotonic
2023-12-10T23:32:07.950510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
32.26 11
22.4%
30.0 6
12.2%
27.8 6
12.2%
28.5 4
 
8.2%
32.25 4
 
8.2%
45.0 4
 
8.2%
18.0 2
 
4.1%
32.0 2
 
4.1%
38.0 2
 
4.1%
36.8 2
 
4.1%
Other values (6) 6
12.2%
ValueCountFrequency (%)
13.75 1
 
2.0%
15.85 1
 
2.0%
18.0 2
 
4.1%
21.6 1
 
2.0%
23.7 1
 
2.0%
27.8 6
12.2%
28.5 4
8.2%
30.0 6
12.2%
32.0 2
 
4.1%
32.25 4
8.2%
ValueCountFrequency (%)
50.0 1
 
2.0%
45.06 1
 
2.0%
45.0 4
 
8.2%
38.0 2
 
4.1%
36.8 2
 
4.1%
32.26 11
22.4%
32.25 4
 
8.2%
32.0 2
 
4.1%
30.0 6
12.2%
28.5 4
 
8.2%

SHIP_LNTH
Real number (ℝ)

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.48776
Minimum90.6
Maximum294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:08.110279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90.6
5-th percentile116
Q1178
median185
Q3223
95-th percentile282.708
Maximum294
Range203.4
Interquartile range (IQR)45

Descriptive statistics

Standard deviation46.667213
Coefficient of variation (CV)0.23630434
Kurtosis0.51580865
Mean197.48776
Median Absolute Deviation (MAD)32
Skewness0.0082482675
Sum9676.9
Variance2177.8288
MonotonicityNot monotonic
2023-12-10T23:32:08.313833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
178.0 6
12.2%
223.0 6
12.2%
172.0 6
12.2%
185.0 4
 
8.2%
183.0 3
 
6.1%
222.0 3
 
6.1%
221.0 2
 
4.1%
116.0 2
 
4.1%
282.0 2
 
4.1%
185.6 1
 
2.0%
Other values (14) 14
28.6%
ValueCountFrequency (%)
90.6 1
 
2.0%
90.67 1
 
2.0%
116.0 2
 
4.1%
119.5 1
 
2.0%
172.0 6
12.2%
178.0 6
12.2%
178.8 1
 
2.0%
182.95 1
 
2.0%
183.0 3
6.1%
185.0 4
8.2%
ValueCountFrequency (%)
294.0 1
 
2.0%
286.9 1
 
2.0%
283.18 1
 
2.0%
282.0 2
 
4.1%
279.02 1
 
2.0%
225.3 1
 
2.0%
223.0 6
12.2%
222.74 1
 
2.0%
222.0 3
6.1%
221.0 2
 
4.1%

SHIP_HGHT
Real number (ℝ)

Distinct26
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.48953
Minimum6.3068
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:08.435160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.3068
5-th percentile9.81395
Q115.09
median18
Q320.1
95-th percentile24.78
Maximum24.9
Range18.5932
Interquartile range (IQR)5.01

Descriptive statistics

Standard deviation4.3776724
Coefficient of variation (CV)0.25030247
Kurtosis0.41822009
Mean17.48953
Median Absolute Deviation (MAD)2.4
Skewness-0.43621282
Sum856.98695
Variance19.164016
MonotonicityNot monotonic
2023-12-10T23:32:08.567284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
15.6 6
 
12.2%
14.7 5
 
10.2%
20.1 4
 
8.2%
18.0 3
 
6.1%
15.1 3
 
6.1%
20.2 3
 
6.1%
24.8 2
 
4.1%
9.81395 2
 
4.1%
18.5 2
 
4.1%
20.7 2
 
4.1%
Other values (16) 17
34.7%
ValueCountFrequency (%)
6.3068 1
 
2.0%
6.61325 1
 
2.0%
9.81395 2
 
4.1%
10.529 1
 
2.0%
14.6 1
 
2.0%
14.7 5
10.2%
14.8 1
 
2.0%
15.09 1
 
2.0%
15.1 3
6.1%
15.6 6
12.2%
ValueCountFrequency (%)
24.9 1
 
2.0%
24.8 2
4.1%
24.75 1
 
2.0%
24.5 1
 
2.0%
24.2 1
 
2.0%
20.7 2
4.1%
20.2 3
6.1%
20.1 4
8.2%
20.05 1
 
2.0%
20.0 1
 
2.0%

SHIP_OWNER_NM
Text

MISSING 

Distinct13
Distinct (%)65.0%
Missing29
Missing (%)59.2%
Memory size524.0 B
2023-12-10T23:32:08.788021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length13.7
Min length9

Characters and Unicode

Total characters274
Distinct characters36
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

Unique8 ?
Unique (%)40.0%

Sample

1st rowAkmar Shpg & Trdg
2nd rowEastern Med
3rd rowEastern Med
4th rowCardiff Marine
5th rowNYK Blkshp Atlnt
ValueCountFrequency (%)
iolcos 3
 
6.8%
marine 3
 
6.8%
sea 3
 
6.8%
traders 3
 
6.8%
hellenic 3
 
6.8%
alpha 2
 
4.5%
bulkers 2
 
4.5%
minerva 2
 
4.5%
med 2
 
4.5%
eastern 2
 
4.5%
Other values (19) 19
43.2%
2023-12-10T23:32:09.100460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 31
 
11.3%
24
 
8.8%
a 24
 
8.8%
r 23
 
8.4%
n 20
 
7.3%
l 17
 
6.2%
s 16
 
5.8%
i 11
 
4.0%
c 10
 
3.6%
d 9
 
3.3%
Other values (26) 89
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 203
74.1%
Uppercase Letter 46
 
16.8%
Space Separator 24
 
8.8%
Other Punctuation 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31
15.3%
a 24
11.8%
r 23
11.3%
n 20
9.9%
l 17
8.4%
s 16
7.9%
i 11
 
5.4%
c 10
 
4.9%
d 9
 
4.4%
o 8
 
3.9%
Other values (9) 34
16.7%
Uppercase Letter
ValueCountFrequency (%)
M 8
17.4%
S 6
13.0%
T 6
13.0%
A 5
10.9%
I 5
10.9%
E 3
 
6.5%
H 3
 
6.5%
B 3
 
6.5%
C 1
 
2.2%
N 1
 
2.2%
Other values (5) 5
10.9%
Space Separator
ValueCountFrequency (%)
24
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 249
90.9%
Common 25
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31
 
12.4%
a 24
 
9.6%
r 23
 
9.2%
n 20
 
8.0%
l 17
 
6.8%
s 16
 
6.4%
i 11
 
4.4%
c 10
 
4.0%
d 9
 
3.6%
M 8
 
3.2%
Other values (24) 80
32.1%
Common
ValueCountFrequency (%)
24
96.0%
& 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31
 
11.3%
24
 
8.8%
a 24
 
8.8%
r 23
 
8.4%
n 20
 
7.3%
l 17
 
6.2%
s 16
 
5.8%
i 11
 
4.0%
c 10
 
3.6%
d 9
 
3.3%
Other values (26) 89
32.5%

DRAFT
Real number (ℝ)

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.137573
Minimum5
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:09.221917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q115.5486
median24.9845
Q330
95-th percentile30
Maximum30
Range25
Interquartile range (IQR)14.4514

Descriptive statistics

Standard deviation9.9566309
Coefficient of variation (CV)0.47103944
Kurtosis-1.1487701
Mean21.137573
Median Absolute Deviation (MAD)5.0155
Skewness-0.70152134
Sum1035.7411
Variance99.134498
MonotonicityNot monotonic
2023-12-10T23:32:09.348471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
30.0 15
30.6%
5.0 10
20.4%
22.0984 2
 
4.1%
29.5231 2
 
4.1%
23.0446 1
 
2.0%
23.8117 1
 
2.0%
17.5503 1
 
2.0%
28.8774 1
 
2.0%
28.8672 1
 
2.0%
28.8625 1
 
2.0%
Other values (14) 14
28.6%
ValueCountFrequency (%)
5.0 10
20.4%
8.0 1
 
2.0%
9.43 1
 
2.0%
15.5486 1
 
2.0%
16.3468 1
 
2.0%
17.4915 1
 
2.0%
17.5202 1
 
2.0%
17.5503 1
 
2.0%
17.6085 1
 
2.0%
18.1535 1
 
2.0%
ValueCountFrequency (%)
30.0 15
30.6%
29.5231 2
 
4.1%
29.0157 1
 
2.0%
28.8774 1
 
2.0%
28.8672 1
 
2.0%
28.8625 1
 
2.0%
28.8437 1
 
2.0%
27.2329 1
 
2.0%
27.2114 1
 
2.0%
24.9845 1
 
2.0%

SHPYRD_NM
Text

MISSING 

Distinct12
Distinct (%)60.0%
Missing29
Missing (%)59.2%
Memory size524.0 B
2023-12-10T23:32:09.526097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length15.4
Min length11

Characters and Unicode

Total characters308
Distinct characters35
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

Unique6 ?
Unique (%)30.0%

Sample

1st rowSungdong SB
2nd rowSPP Sacheon SY
3rd rowSPP Sacheon SY
4th rowSCS Shipbuilding
5th rowTsuneishi Zosen
ValueCountFrequency (%)
new 4
 
8.0%
hi 4
 
8.0%
hyundai 4
 
8.0%
hudong 3
 
6.0%
zhonghua 3
 
6.0%
jiangsu 3
 
6.0%
yzj 3
 
6.0%
shipbuilding 3
 
6.0%
tsuneishi 2
 
4.0%
sb 2
 
4.0%
Other values (13) 19
38.0%
2023-12-10T23:32:09.893736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
 
9.7%
n 27
 
8.8%
i 24
 
7.8%
a 23
 
7.5%
u 20
 
6.5%
S 19
 
6.2%
h 18
 
5.8%
g 16
 
5.2%
o 14
 
4.5%
e 12
 
3.9%
Other values (25) 105
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 202
65.6%
Uppercase Letter 72
 
23.4%
Space Separator 30
 
9.7%
Open Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 27
13.4%
i 24
11.9%
a 23
11.4%
u 20
9.9%
h 18
8.9%
g 16
7.9%
o 14
6.9%
e 12
5.9%
s 11
5.4%
d 11
5.4%
Other values (8) 26
12.9%
Uppercase Letter
ValueCountFrequency (%)
S 19
26.4%
H 11
15.3%
Z 7
 
9.7%
J 6
 
8.3%
Y 5
 
6.9%
P 4
 
5.6%
N 4
 
5.6%
I 4
 
5.6%
C 3
 
4.2%
T 3
 
4.2%
Other values (4) 6
 
8.3%
Space Separator
ValueCountFrequency (%)
30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 274
89.0%
Common 34
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 27
 
9.9%
i 24
 
8.8%
a 23
 
8.4%
u 20
 
7.3%
S 19
 
6.9%
h 18
 
6.6%
g 16
 
5.8%
o 14
 
5.1%
e 12
 
4.4%
s 11
 
4.0%
Other values (22) 90
32.8%
Common
ValueCountFrequency (%)
30
88.2%
( 2
 
5.9%
) 2
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
 
9.7%
n 27
 
8.8%
i 24
 
7.8%
a 23
 
7.5%
u 20
 
6.5%
S 19
 
6.2%
h 18
 
5.8%
g 16
 
5.2%
o 14
 
4.5%
e 12
 
3.9%
Other values (25) 105
34.1%

BULD_YR
Real number (ℝ)

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.0204
Minimum1997
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:10.307790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2009.4
Q12012
median2012
Q32013
95-th percentile2017.6
Maximum2019
Range22
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.6713406
Coefficient of variation (CV)0.0018247034
Kurtosis10.912515
Mean2012.0204
Median Absolute Deviation (MAD)1
Skewness-2.5424789
Sum98589
Variance13.478741
MonotonicityNot monotonic
2023-12-10T23:32:10.410080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2012 19
38.8%
2013 17
34.7%
2011 4
 
8.2%
1997 2
 
4.1%
2019 2
 
4.1%
2010 2
 
4.1%
2017 1
 
2.0%
2018 1
 
2.0%
2009 1
 
2.0%
ValueCountFrequency (%)
1997 2
 
4.1%
2009 1
 
2.0%
2010 2
 
4.1%
2011 4
 
8.2%
2012 19
38.8%
2013 17
34.7%
2017 1
 
2.0%
2018 1
 
2.0%
2019 2
 
4.1%
ValueCountFrequency (%)
2019 2
 
4.1%
2018 1
 
2.0%
2017 1
 
2.0%
2013 17
34.7%
2012 19
38.8%
2011 4
 
8.2%
2010 2
 
4.1%
2009 1
 
2.0%
1997 2
 
4.1%

DDWGHT
Real number (ℝ)

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67507.98
Minimum2800
Maximum206026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:10.519450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2800
5-th percentile7950
Q136722
median57000
Q381585
95-th percentile177372.6
Maximum206026
Range203226
Interquartile range (IQR)44863

Descriptive statistics

Standard deviation49683.019
Coefficient of variation (CV)0.73595773
Kurtosis1.2740736
Mean67507.98
Median Absolute Deviation (MAD)24247
Skewness1.3133725
Sum3307891
Variance2.4684024 × 109
MonotonicityNot monotonic
2023-12-10T23:32:10.707617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
57000 3
 
6.1%
36940 3
 
6.1%
87376 2
 
4.1%
7950 2
 
4.1%
37587 2
 
4.1%
76094 1
 
2.0%
63250 1
 
2.0%
63200 1
 
2.0%
35176 1
 
2.0%
175191 1
 
2.0%
Other values (32) 32
65.3%
ValueCountFrequency (%)
2800 1
2.0%
3250 1
2.0%
7950 2
4.1%
9000 1
2.0%
30679 1
2.0%
33800 1
2.0%
34563 1
2.0%
35139 1
2.0%
35157 1
2.0%
35173 1
2.0%
ValueCountFrequency (%)
206026 1
2.0%
180643 1
2.0%
177933 1
2.0%
176532 1
2.0%
175191 1
2.0%
169233 1
2.0%
93269 1
2.0%
93257 1
2.0%
87376 2
4.1%
82099 1
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:45
Maximum2022-01-10 06:39:41
2023-12-10T23:32:10.839400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:10.981214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

ARVL_HMS
Date

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-06-05 16:59:22
Maximum2022-07-17 21:59:40
2023-12-10T23:32:11.122385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:32:11.266883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.414245
Minimum-33.029301
Maximum57.7784
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)20.4%
Memory size573.0 B
2023-12-10T23:32:11.473552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-33.029301
5-th percentile-31.89856
Q18.70224
median29.893801
Q336.7234
95-th percentile52.0785
Maximum57.7784
Range90.807701
Interquartile range (IQR)28.02116

Descriptive statistics

Standard deviation26.674516
Coefficient of variation (CV)1.3066619
Kurtosis-0.39394875
Mean20.414245
Median Absolute Deviation (MAD)12.605101
Skewness-0.87650249
Sum1000.298
Variance711.52979
MonotonicityNot monotonic
2023-12-10T23:32:11.671556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
30.4235 1
 
2.0%
29.608999 1
 
2.0%
19.473801 1
 
2.0%
35.7122 1
 
2.0%
-20.330601 1
 
2.0%
17.2887 1
 
2.0%
30.425501 1
 
2.0%
8.70224 1
 
2.0%
49.992599 1
 
2.0%
36.7234 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-33.029301 1
2.0%
-33.0098 1
2.0%
-32.744999 1
2.0%
-30.628901 1
2.0%
-30.094801 1
2.0%
-24.237801 1
2.0%
-23.811701 1
2.0%
-20.330601 1
2.0%
-20.087601 1
2.0%
-6.72754 1
2.0%
ValueCountFrequency (%)
57.7784 1
2.0%
53.950001 1
2.0%
52.202499 1
2.0%
51.892502 1
2.0%
50.7962 1
2.0%
49.992599 1
2.0%
43.402901 1
2.0%
43.3983 1
2.0%
43.014999 1
2.0%
40.843601 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.278431
Minimum-94.647003
Maximum153.49699
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)38.8%
Memory size573.0 B
2023-12-10T23:32:11.813774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-94.647003
5-th percentile-83.423264
Q1-11.2216
median4.93667
Q392.200798
95-th percentile132.97
Maximum153.49699
Range248.144
Interquartile range (IQR)103.4224

Descriptive statistics

Standard deviation71.979398
Coefficient of variation (CV)3.2309007
Kurtosis-0.92719392
Mean22.278431
Median Absolute Deviation (MAD)36.604971
Skewness0.2792836
Sum1091.6431
Variance5181.0338
MonotonicityNot monotonic
2023-12-10T23:32:11.953619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-9.63856 1
 
2.0%
-89.866302 1
 
2.0%
92.200798 1
 
2.0%
-0.639248 1
 
2.0%
38.689301 1
 
2.0%
86.313301 1
 
2.0%
-9.64748 1
 
2.0%
-86.086304 1
 
2.0%
-66.791 1
 
2.0%
5.16162 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-94.647003 1
2.0%
-89.866302 1
2.0%
-86.086304 1
2.0%
-79.428703 1
2.0%
-78.292198 1
2.0%
-71.5923 1
2.0%
-71.521698 1
2.0%
-66.791 1
2.0%
-64.783302 1
2.0%
-31.668301 1
2.0%
ValueCountFrequency (%)
153.496994 1
2.0%
151.477997 1
2.0%
135.139999 1
2.0%
129.714996 1
2.0%
126.767998 1
2.0%
122.239998 1
2.0%
122.031998 1
2.0%
122.023003 1
2.0%
120.227997 1
2.0%
118.633003 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.65756
Minimum-33.730999
Maximum57.3703
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)24.5%
Memory size573.0 B
2023-12-10T23:32:12.114568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-33.730999
5-th percentile-26.761219
Q12.505
median28.087601
Q340.625
95-th percentile52.63794
Maximum57.3703
Range91.101299
Interquartile range (IQR)38.12

Descriptive statistics

Standard deviation24.879269
Coefficient of variation (CV)1.2043663
Kurtosis-0.50014905
Mean20.65756
Median Absolute Deviation (MAD)15.300499
Skewness-0.66098695
Sum1012.2204
Variance618.97803
MonotonicityNot monotonic
2023-12-10T23:32:12.255046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
27.094999 1
 
2.0%
-27.508699 1
 
2.0%
51.147999 1
 
2.0%
-2.35677 1
 
2.0%
-19.6677 1
 
2.0%
-33.659801 1
 
2.0%
32.1521 1
 
2.0%
26.665899 1
 
2.0%
-1.315 1
 
2.0%
-33.730999 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-33.730999 1
2.0%
-33.659801 1
2.0%
-27.508699 1
2.0%
-25.639999 1
2.0%
-20.579201 1
2.0%
-19.6677 1
2.0%
-6.32167 1
2.0%
-5.005 1
2.0%
-2.35677 1
2.0%
-2.12167 1
2.0%
ValueCountFrequency (%)
57.3703 1
2.0%
54.373402 1
2.0%
53.1227 1
2.0%
51.910801 1
2.0%
51.147999 1
2.0%
46.077202 1
2.0%
43.403 1
2.0%
43.392899 1
2.0%
43.3881 1
2.0%
43.3517 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.924203
Minimum-118.207
Maximum148.198
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)32.7%
Memory size573.0 B
2023-12-10T23:32:12.392787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-118.207
5-th percentile-69.989559
Q1-9.1733
median11.5446
Q368.831703
95-th percentile128.6034
Maximum148.198
Range266.405
Interquartile range (IQR)78.005003

Descriptive statistics

Standard deviation64.471225
Coefficient of variation (CV)2.3945454
Kurtosis-0.49574027
Mean26.924203
Median Absolute Deviation (MAD)33.0258
Skewness0.1650254
Sum1319.2859
Variance4156.5389
MonotonicityNot monotonic
2023-12-10T23:32:12.523830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-13.43 1
 
2.0%
44.5704 1
 
2.0%
1.6439 1
 
2.0%
-44.339001 1
 
2.0%
148.061996 1
 
2.0%
11.5446 1
 
2.0%
-9.81275 1
 
2.0%
-14.548 1
 
2.0%
54.9067 1
 
2.0%
-59.361401 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-118.207001 1
2.0%
-86.834602 1
2.0%
-77.074997 1
2.0%
-59.361401 1
2.0%
-48.751701 1
2.0%
-48.59 1
2.0%
-48.158298 1
2.0%
-44.339001 1
2.0%
-16.997101 1
2.0%
-14.548 1
2.0%
ValueCountFrequency (%)
148.197998 1
2.0%
148.061996 1
2.0%
132.684998 1
2.0%
122.481003 1
2.0%
120.911003 1
2.0%
118.728996 1
2.0%
117.407997 1
2.0%
117.199997 1
2.0%
108.958 1
2.0%
104.977997 1
2.0%

NVGTN_TM
Real number (ℝ)

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4565.2827
Minimum3562.81
Maximum4725.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:12.657315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3562.81
5-th percentile3715.388
Q14646.31
median4721.95
Q34725.44
95-th percentile4725.932
Maximum4725.97
Range1163.16
Interquartile range (IQR)79.13

Descriptive statistics

Standard deviation345.06638
Coefficient of variation (CV)0.075584889
Kurtosis3.4349742
Mean4565.2827
Median Absolute Deviation (MAD)3.97
Skewness-2.2340628
Sum223698.85
Variance119070.81
MonotonicityNot monotonic
2023-12-10T23:32:12.796016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4725.97 2
 
4.1%
4725.67 2
 
4.1%
4723.39 1
 
2.0%
4725.83 1
 
2.0%
4725.92 1
 
2.0%
3719.71 1
 
2.0%
4721.95 1
 
2.0%
4725.94 1
 
2.0%
4724.26 1
 
2.0%
4705.75 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
3562.81 1
2.0%
3594.52 1
2.0%
3712.96 1
2.0%
3719.03 1
2.0%
3719.48 1
2.0%
3719.71 1
2.0%
4430.29 1
2.0%
4501.29 1
2.0%
4506.36 1
2.0%
4523.08 1
2.0%
ValueCountFrequency (%)
4725.97 2
4.1%
4725.94 1
2.0%
4725.92 1
2.0%
4725.86 1
2.0%
4725.85 1
2.0%
4725.84 1
2.0%
4725.83 1
2.0%
4725.82 1
2.0%
4725.67 2
4.1%
4725.63 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

2023-12-10T23:32:12.913989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:13.005464image/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%
Mean225.24654
Minimum0.963611
Maximum1151.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:13.103364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.963611
5-th percentile2.588948
Q126.6053
median110
Q3316.165
95-th percentile775.6714
Maximum1151.56
Range1150.5964
Interquartile range (IQR)289.5597

Descriptive statistics

Standard deviation278.2706
Coefficient of variation (CV)1.2354045
Kurtosis2.7048254
Mean225.24654
Median Absolute Deviation (MAD)101.39806
Skewness1.7375631
Sum11037.081
Variance77434.527
MonotonicityNot monotonic
2023-12-10T23:32:13.266468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10.7931 1
 
2.0%
306.657 1
 
2.0%
635.145 1
 
2.0%
58.8569 1
 
2.0%
609.16 1
 
2.0%
233.903 1
 
2.0%
26.7108 1
 
2.0%
159.697 1
 
2.0%
234.22 1
 
2.0%
433.837 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%
3.88028 1
2.0%
4.16667 1
2.0%
5.51639 1
2.0%
8.60194 1
2.0%
10.7931 1
2.0%
12.3517 1
2.0%
14.7386 1
2.0%
ValueCountFrequency (%)
1151.56 1
2.0%
1050.54 1
2.0%
798.841 1
2.0%
740.917 1
2.0%
635.145 1
2.0%
632.743 1
2.0%
609.16 1
2.0%
462.496 1
2.0%
433.837 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%
Mean4340.0359
Minimum3110.55
Maximum4723.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:13.417143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3110.55
5-th percentile3421.852
Q14262.18
median4509.84
Q34661.65
95-th percentile4712.888
Maximum4723.68
Range1613.13
Interquartile range (IQR)399.47

Descriptive statistics

Standard deviation449.23348
Coefficient of variation (CV)0.10350916
Kurtosis0.66888652
Mean4340.0359
Median Absolute Deviation (MAD)157.79
Skewness-1.3715725
Sum212661.76
Variance201810.72
MonotonicityNot monotonic
2023-12-10T23:32:13.568547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4712.6 1
 
2.0%
4419.17 1
 
2.0%
4087.67 1
 
2.0%
4667.07 1
 
2.0%
3110.55 1
 
2.0%
4488.04 1
 
2.0%
4699.23 1
 
2.0%
4564.56 1
 
2.0%
4471.53 1
 
2.0%
4262.24 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3110.55 1
2.0%
3263.12 1
2.0%
3396.8 1
2.0%
3459.43 1
2.0%
3499.71 1
2.0%
3548.1 1
2.0%
3678.19 1
2.0%
3710.87 1
2.0%
3782.16 1
2.0%
3920.03 1
2.0%
ValueCountFrequency (%)
4723.68 1
2.0%
4719.48 1
2.0%
4713.08 1
2.0%
4712.6 1
2.0%
4706.24 1
2.0%
4703.47 1
2.0%
4700.31 1
2.0%
4700.29 1
2.0%
4699.23 1
2.0%
4676.6 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

2023-12-10T23:32:13.701466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:13.796132image/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
2023-12-10T23:32:13.914059image/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
2023-12-10T23:32:14.352381image/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_LONVGTN_TMPRT_NCHRG_TMBLLAT_HOURFRGHT_CNVNC_TMNNVGTN_TMRN
02280846009777541MAYOURYChemical/Oil Product21.6119.510.529<NA>23.0446<NA>2017900001-Jan-2022 00:01:4117-Jul-2022 19:25:1230.4235-9.6385627.094999-13.434723.39010.79314712.602
12282008009163570CAPO NEROCement Carrier13.7590.66.3068<NA>15.5486<NA>1997280005-Jan-2022 12:15:4417-Jul-2022 21:59:4043.4029015.0066943.4035.019874617.7304.166674613.5703
22283668009837341HYDRAChemical/Oil Product18.0116.09.81395<NA>22.0984<NA>2018795001-Jan-2022 00:56:0617-Jul-2022 21:58:3743.39834.9366743.3928994.963234725.040110.04615.0404
32283726009837353SEDNAChemical/Oil Product18.0116.09.81395<NA>22.0984<NA>2019795001-Jan-2022 00:55:0617-Jul-2022 21:56:0943.0149993.1466743.38814.953434725.02024.72974700.2905
42283729009103790CAPO CINTOCement Carrier15.8590.676.61325<NA>16.3468<NA>1997325007-Jan-2022 07:49:2105-Jun-2022 18:38:0540.84360114.267643.35175.318333562.810103.3823459.4306
52290030009621869ADFINES NORTHBULK CARRIER28.5182.9515.09<NA>29.0157<NA>20123694101-Jan-2022 00:06:5217-Jul-2022 03:27:4450.79621.44952-1.535-48.7517014707.3503.880284703.4707
62290100009621895ADFINES WESTBULK CARRIER28.5183.015.1<NA>5.0<NA>20123694001-Jan-2022 00:56:4617-Jul-2022 21:56:2936.433316.211743.19749827.78484724.9905.516394719.4808
72290110009621871ADFINES SOUTHBULK CARRIER28.5183.015.1<NA>5.0<NA>20113694001-Jan-2022 00:05:0517-Jul-2022 21:42:4426.2335-15.270723.8234-16.9971014725.63062.02174663.6109
82290120009621883ADFINES EASTBULK CARRIER28.5183.015.1<NA>5.0<NA>20123694001-Jan-2022 00:08:4717-Jul-2022 18:56:0739.444599-0.31975754.37340218.6599014722.790117.0334605.75010
92290130009498274RODOPIBULK CARRIER23.7178.814.6<NA>24.0971<NA>20123067901-Jan-2022 00:54:4617-Jul-2022 21:35:2353.950001-6.0033328.1817-48.594724.680462.4964262.18011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LONVGTN_TMPRT_NCHRG_TMBLLAT_HOURFRGHT_CNVNC_TMNNVGTN_TMRN
392292000009649110DENSA SEALBULK CARRIER27.8178.015.6<NA>29.5231<NA>20133758701-Jan-2022 01:07:4417-Jul-2022 21:50:2357.77849.837533.89357-77.0749974724.7101.030564723.68041
402292010009649108DENSA PUMABULK CARRIER27.8178.015.6<NA>28.8437<NA>20133672201-Jan-2022 00:06:0914-Jul-2022 14:24:49-33.0098-71.592328.087601-86.8346024646.31091.84644554.47042
412292020009649093DENSA HAWKBULK CARRIER27.8178.015.6<NA>28.8625<NA>20133674601-Jan-2022 00:03:2617-Jul-2022 21:55:1352.2024994.2505846.077202-1.251964725.86025.54894700.31043
422292030009649081DENSA FALCONBULK CARRIER27.8178.015.6<NA>28.8672<NA>20133675201-Jan-2022 07:25:0917-Jul-2022 21:52:5828.0714135.13999938.6982-9.17334718.470127.8744590.59044
432292040009649079DENSA SEA LIONBULK CARRIER27.8178.015.6<NA>28.8774<NA>20133676501-Jan-2022 00:40:0717-Jul-2022 21:58:5710.1567-79.4287037.28333108.9584725.310373.2684352.05045
442292070009476460Mayfair SpiritBulk Carrier38.0222.020.7Unimar Success30.0Jiangsu New YZJ20119325706-Jan-2022 03:58:5905-Jun-2022 22:30:254.12733100.58200157.370319.83583594.520331.4053263.12046
452292120009455686MonemvasiaBulk Carrier45.0282.024.8Minerva Marine17.5503Shanghai Waigaoqiao200917793301-Jan-2022 00:00:5117-Jul-2022 21:59:11-20.087601118.63300315.258365.1532974725.97064.31974661.65047
462292130009625475DOGANBULK CARRIER30.0172.014.7<NA>5.0<NA>20133517301-Jan-2022 00:09:2505-Jul-2022 14:26:5129.89380132.53799832.81000135.0304994430.2901.728064428.56048
472292140009625463NEDIMBULK CARRIER30.0172.014.7<NA>5.0<NA>20133515701-Jan-2022 00:02:3417-Jul-2022 21:51:57-30.094801153.49699436.47330118.3467014725.82049.21584676.6049
482292150009625451ORHANBULK CARRIER30.0172.014.7<NA>5.0<NA>20133513901-Jan-2022 01:10:4917-Jul-2022 16:02:4510.1933-64.783302-25.639999-48.1582984718.870798.8413920.03050