Overview

Dataset statistics

Number of variables24
Number of observations49
Missing cells46
Missing cells (%)3.9%
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_001049

Alerts

NNVGTN_TM has constant value ""Constant
PRT_NCHRG_TM is highly imbalanced (85.6%)Imbalance
SHIP_OWNER_NM has 23 (46.9%) missing valuesMissing
SHPYRD_NM has 23 (46.9%) missing valuesMissing
MMSI has unique valuesUnique
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM has unique valuesUnique
DPTR_HMS has unique valuesUnique
ARVL_HMS has unique valuesUnique
DPTRP_LA has unique valuesUnique
DPTRP_LO has unique valuesUnique
DTNT_LA has unique valuesUnique
DTNT_LO has unique valuesUnique
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 14:26:49.333727
Analysis finished2023-12-10 14:26:49.636302
Duration0.3 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%
Mean2.2936357 × 108
Minimum2.29219 × 108
Maximum2.29534 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:49.699545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.29219 × 108
5-th percentile2.29227 × 108
Q12.29283 × 108
median2.29364 × 108
Q32.29411 × 108
95-th percentile2.295316 × 108
Maximum2.29534 × 108
Range315000
Interquartile range (IQR)128000

Descriptive statistics

Standard deviation96417.063
Coefficient of variation (CV)0.00042036781
Kurtosis-0.88548924
Mean2.2936357 × 108
Median Absolute Deviation (MAD)79000
Skewness0.3908454
Sum1.1238815 × 1010
Variance9.29625 × 109
MonotonicityStrictly increasing
2023-12-10T23:26:49.801791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
229219000 1
 
2.0%
229421000 1
 
2.0%
229380000 1
 
2.0%
229381000 1
 
2.0%
229382000 1
 
2.0%
229387000 1
 
2.0%
229395000 1
 
2.0%
229396000 1
 
2.0%
229397000 1
 
2.0%
229409000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
229219000 1
2.0%
229220000 1
2.0%
229221000 1
2.0%
229236000 1
2.0%
229243000 1
2.0%
229264000 1
2.0%
229265000 1
2.0%
229266000 1
2.0%
229269000 1
2.0%
229276000 1
2.0%
ValueCountFrequency (%)
229534000 1
2.0%
229533000 1
2.0%
229532000 1
2.0%
229531000 1
2.0%
229530000 1
2.0%
229529000 1
2.0%
229523000 1
2.0%
229486000 1
2.0%
229478000 1
2.0%
229462000 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9551507.6
Minimum9118678
Maximum9721683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:49.927406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9118678
5-th percentile9218878
Q19490777
median9609469
Q39644201
95-th percentile9666005.4
Maximum9721683
Range603005
Interquartile range (IQR)153424

Descriptive statistics

Standard deviation146977.14
Coefficient of variation (CV)0.015387847
Kurtosis1.7317082
Mean9551507.6
Median Absolute Deviation (MAD)37426
Skewness-1.5756972
Sum4.6802387 × 108
Variance2.160228 × 1010
MonotonicityNot monotonic
2023-12-10T23:26:50.032497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9644201 1
 
2.0%
9118678 1
 
2.0%
9311177 1
 
2.0%
9266140 1
 
2.0%
9479204 1
 
2.0%
9587257 1
 
2.0%
9490777 1
 
2.0%
9490868 1
 
2.0%
9445772 1
 
2.0%
9721671 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9118678 1
2.0%
9153056 1
2.0%
9187370 1
2.0%
9266140 1
2.0%
9311177 1
2.0%
9315537 1
2.0%
9323900 1
2.0%
9445772 1
2.0%
9457854 1
2.0%
9464651 1
2.0%
ValueCountFrequency (%)
9721683 1
2.0%
9721671 1
2.0%
9668403 1
2.0%
9662409 1
2.0%
9662332 1
2.0%
9662320 1
2.0%
9657777 1
2.0%
9650171 1
2.0%
9646900 1
2.0%
9646895 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:26:50.216677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length9.8571429
Min length4

Characters and Unicode

Total characters483
Distinct characters46
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 rowIZMIR
2nd rowSAMSUN
3rd rowSIIRT
4th rowFEDERAL MATTAWA
5th rowFlag Seaman
ValueCountFrequency (%)
la 4
 
5.3%
lbc 2
 
2.6%
flag 2
 
2.6%
thalassini 2
 
2.6%
nba 2
 
2.6%
js 2
 
2.6%
cano 1
 
1.3%
topeka 1
 
1.3%
seaunity 1
 
1.3%
pomerol 1
 
1.3%
Other values (58) 58
76.3%
2023-12-10T23:26:50.509635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 39
 
8.1%
27
 
5.6%
I 24
 
5.0%
a 24
 
5.0%
S 22
 
4.6%
L 22
 
4.6%
i 21
 
4.3%
n 21
 
4.3%
e 20
 
4.1%
O 20
 
4.1%
Other values (36) 243
50.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 269
55.7%
Lowercase Letter 187
38.7%
Space Separator 27
 
5.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 39
14.5%
I 24
 
8.9%
S 22
 
8.2%
L 22
 
8.2%
O 20
 
7.4%
R 18
 
6.7%
N 16
 
5.9%
E 15
 
5.6%
G 13
 
4.8%
B 13
 
4.8%
Other values (13) 67
24.9%
Lowercase Letter
ValueCountFrequency (%)
a 24
12.8%
i 21
11.2%
n 21
11.2%
e 20
10.7%
o 18
9.6%
t 11
 
5.9%
s 11
 
5.9%
l 10
 
5.3%
r 9
 
4.8%
h 8
 
4.3%
Other values (12) 34
18.2%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 456
94.4%
Common 27
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 39
 
8.6%
I 24
 
5.3%
a 24
 
5.3%
S 22
 
4.8%
L 22
 
4.8%
i 21
 
4.6%
n 21
 
4.6%
e 20
 
4.4%
O 20
 
4.4%
R 18
 
3.9%
Other values (35) 225
49.3%
Common
ValueCountFrequency (%)
27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 39
 
8.1%
27
 
5.6%
I 24
 
5.0%
a 24
 
5.0%
S 22
 
4.6%
L 22
 
4.6%
i 21
 
4.3%
n 21
 
4.3%
e 20
 
4.1%
O 20
 
4.1%
Other values (36) 243
50.3%

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

2023-12-10T23:26:50.632598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:26:50.722267image/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)

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.364082
Minimum23.5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:50.796690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile28.34
Q132
median32.26
Q343
95-th percentile46
Maximum50
Range26.5
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.7528179
Coefficient of variation (CV)0.19095132
Kurtosis-0.65779249
Mean35.364082
Median Absolute Deviation (MAD)2.26
Skewness0.7711208
Sum1732.84
Variance45.60055
MonotonicityNot monotonic
2023-12-10T23:26:50.882967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
32.26 15
30.6%
45.0 7
14.3%
30.0 4
 
8.2%
32.2 3
 
6.1%
32.0 2
 
4.1%
43.0 2
 
4.1%
36.5 2
 
4.1%
28.4 2
 
4.1%
50.0 2
 
4.1%
28.3 2
 
4.1%
Other values (5) 8
16.3%
ValueCountFrequency (%)
23.5 1
 
2.0%
28.3 2
 
4.1%
28.4 2
 
4.1%
28.6 2
 
4.1%
30.0 4
 
8.2%
32.0 2
 
4.1%
32.2 3
 
6.1%
32.24 1
 
2.0%
32.26 15
30.6%
36.5 2
 
4.1%
ValueCountFrequency (%)
50.0 2
 
4.1%
46.0 2
 
4.1%
45.0 7
14.3%
43.0 2
 
4.1%
38.0 2
 
4.1%
36.5 2
 
4.1%
32.26 15
30.6%
32.24 1
 
2.0%
32.2 3
 
6.1%
32.0 2
 
4.1%

SHIP_LNTH
Real number (ℝ)

Distinct26
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.98939
Minimum168.5
Maximum294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:50.970073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168.5
5-th percentile176.26
Q1185
median215
Q3245.62
95-th percentile287.404
Maximum294
Range125.5
Interquartile range (IQR)60.62

Descriptive statistics

Standard deviation41.338707
Coefficient of variation (CV)0.18877037
Kurtosis-0.93764467
Mean218.98939
Median Absolute Deviation (MAD)30.62
Skewness0.69509657
Sum10730.48
Variance1708.8887
MonotonicityNot monotonic
2023-12-10T23:26:51.071028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
217.0 4
 
8.2%
225.5 4
 
8.2%
176.65 4
 
8.2%
185.0 4
 
8.2%
222.0 3
 
6.1%
193.74 3
 
6.1%
294.0 2
 
4.1%
286.66 2
 
4.1%
282.0 2
 
4.1%
205.0 2
 
4.1%
Other values (16) 19
38.8%
ValueCountFrequency (%)
168.5 1
 
2.0%
171.5 1
 
2.0%
176.0 1
 
2.0%
176.65 4
8.2%
178.0 2
4.1%
181.0 1
 
2.0%
183.0 2
4.1%
185.0 4
8.2%
185.34 1
 
2.0%
193.74 3
6.1%
ValueCountFrequency (%)
294.0 2
4.1%
287.9 1
 
2.0%
286.66 2
4.1%
285.0 2
4.1%
283.8 1
 
2.0%
282.2 1
 
2.0%
282.0 2
4.1%
248.0 1
 
2.0%
245.62 1
 
2.0%
225.5 4
8.2%

SHIP_HGHT
Real number (ℝ)

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.366327
Minimum14.1
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:51.189086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.1
5-th percentile14.55
Q117.7
median18.6
Q320.7
95-th percentile24.8
Maximum24.9
Range10.8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4677927
Coefficient of variation (CV)0.17906301
Kurtosis-0.87240664
Mean19.366327
Median Absolute Deviation (MAD)2.1
Skewness0.36097364
Sum948.95
Variance12.025586
MonotonicityNot monotonic
2023-12-10T23:26:51.509935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
24.8 6
 
12.2%
20.05 5
 
10.2%
18.5 4
 
8.2%
18.0 4
 
8.2%
15.0 4
 
8.2%
17.7 2
 
4.1%
24.9 2
 
4.1%
20.7 2
 
4.1%
14.1 2
 
4.1%
18.6 2
 
4.1%
Other values (15) 16
32.7%
ValueCountFrequency (%)
14.1 2
4.1%
14.25 1
 
2.0%
15.0 4
8.2%
15.2 1
 
2.0%
15.21 1
 
2.0%
15.6 2
4.1%
16.5 1
 
2.0%
17.7 2
4.1%
18.0 4
8.2%
18.1 1
 
2.0%
ValueCountFrequency (%)
24.9 2
 
4.1%
24.8 6
12.2%
24.75 1
 
2.0%
24.7 1
 
2.0%
24.5 1
 
2.0%
20.7 2
 
4.1%
20.2 1
 
2.0%
20.05 5
10.2%
19.9 1
 
2.0%
19.7 1
 
2.0%

SHIP_OWNER_NM
Text

MISSING 

Distinct15
Distinct (%)57.7%
Missing23
Missing (%)46.9%
Memory size524.0 B
2023-12-10T23:26:51.665014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length12.576923
Min length8

Characters and Unicode

Total characters327
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)42.3%

Sample

1st rowGolden Union
2nd rowNYK Blkshp Atlnt
3rd rowNYK Blkshp Atlnt
4th rowCardiff Marine
5th rowSea Traders
ValueCountFrequency (%)
sea 6
 
11.3%
traders 6
 
11.3%
nyk 4
 
7.5%
blkshp 4
 
7.5%
atlnt 4
 
7.5%
dryships 3
 
5.7%
sa 3
 
5.7%
enesel 2
 
3.8%
marine 2
 
3.8%
bulkers 1
 
1.9%
Other values (18) 18
34.0%
2023-12-10T23:26:51.985704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 30
 
9.2%
27
 
8.3%
r 27
 
8.3%
a 24
 
7.3%
s 23
 
7.0%
n 19
 
5.8%
i 18
 
5.5%
l 17
 
5.2%
t 14
 
4.3%
h 12
 
3.7%
Other values (29) 116
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 235
71.9%
Uppercase Letter 64
 
19.6%
Space Separator 27
 
8.3%
Other Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30
12.8%
r 27
11.5%
a 24
10.2%
s 23
9.8%
n 19
8.1%
i 18
7.7%
l 17
7.2%
t 14
 
6.0%
h 12
 
5.1%
d 11
 
4.7%
Other values (10) 40
17.0%
Uppercase Letter
ValueCountFrequency (%)
S 9
14.1%
A 8
12.5%
T 7
10.9%
M 7
10.9%
B 5
7.8%
K 4
 
6.2%
Y 4
 
6.2%
N 4
 
6.2%
D 3
 
4.7%
E 3
 
4.7%
Other values (7) 10
15.6%
Space Separator
ValueCountFrequency (%)
27
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 299
91.4%
Common 28
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30
 
10.0%
r 27
 
9.0%
a 24
 
8.0%
s 23
 
7.7%
n 19
 
6.4%
i 18
 
6.0%
l 17
 
5.7%
t 14
 
4.7%
h 12
 
4.0%
d 11
 
3.7%
Other values (27) 104
34.8%
Common
ValueCountFrequency (%)
27
96.4%
. 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30
 
9.2%
27
 
8.3%
r 27
 
8.3%
a 24
 
7.3%
s 23
 
7.0%
n 19
 
5.8%
i 18
 
5.5%
l 17
 
5.2%
t 14
 
4.3%
h 12
 
3.7%
Other values (29) 116
35.5%

DRAFT
Real number (ℝ)

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.462808
Minimum3
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:52.123699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.4
Q117.6239
median27.2114
Q330
95-th percentile30
Maximum30
Range27
Interquartile range (IQR)12.3761

Descriptive statistics

Standard deviation8.0385759
Coefficient of variation (CV)0.34260929
Kurtosis0.61907204
Mean23.462808
Median Absolute Deviation (MAD)2.7886
Skewness-1.1933276
Sum1149.6776
Variance64.618703
MonotonicityNot monotonic
2023-12-10T23:26:52.230534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
30.0 21
42.9%
27.2114 3
 
6.1%
17.5907 2
 
4.1%
21.0 2
 
4.1%
4.0 2
 
4.1%
5.0 1
 
2.0%
24.3353 1
 
2.0%
27.3311 1
 
2.0%
24.4033 1
 
2.0%
17.5876 1
 
2.0%
Other values (14) 14
28.6%
ValueCountFrequency (%)
3.0 1
2.0%
4.0 2
4.1%
5.0 1
2.0%
9.0 1
2.0%
17.49 1
2.0%
17.5141 1
2.0%
17.5187 1
2.0%
17.585 1
2.0%
17.5876 1
2.0%
17.5907 2
4.1%
ValueCountFrequency (%)
30.0 21
42.9%
29.0691 1
 
2.0%
27.3311 1
 
2.0%
27.2114 3
 
6.1%
25.2942 1
 
2.0%
24.4033 1
 
2.0%
24.3378 1
 
2.0%
24.3353 1
 
2.0%
21.8201 1
 
2.0%
21.0 2
 
4.1%

SHPYRD_NM
Text

MISSING 

Distinct13
Distinct (%)50.0%
Missing23
Missing (%)46.9%
Memory size524.0 B
2023-12-10T23:26:52.386460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length15.692308
Min length12

Characters and Unicode

Total characters408
Distinct characters38
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 (%)23.1%

Sample

1st rowSCS Shipbuilding
2nd rowOshima Shipbuilding
3rd rowOshima Shipbuilding
4th rowSCS Shipbuilding
5th rowNew Century SB
ValueCountFrequency (%)
new 8
 
12.3%
sb 8
 
12.3%
shipbuilding 7
 
10.8%
century 5
 
7.7%
scs 4
 
6.2%
oshima 3
 
4.6%
beihai 2
 
3.1%
hi 2
 
3.1%
tianjin 2
 
3.1%
shipyard 2
 
3.1%
Other values (17) 22
33.8%
2023-12-10T23:26:52.655809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 46
 
11.3%
39
 
9.6%
n 32
 
7.8%
S 29
 
7.1%
e 23
 
5.6%
u 21
 
5.1%
a 18
 
4.4%
h 18
 
4.4%
g 18
 
4.4%
s 14
 
3.4%
Other values (28) 150
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 272
66.7%
Uppercase Letter 93
 
22.8%
Space Separator 39
 
9.6%
Open Punctuation 2
 
0.5%
Close Punctuation 2
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 46
16.9%
n 32
11.8%
e 23
 
8.5%
u 21
 
7.7%
a 18
 
6.6%
h 18
 
6.6%
g 18
 
6.6%
s 14
 
5.1%
d 10
 
3.7%
p 9
 
3.3%
Other values (10) 63
23.2%
Uppercase Letter
ValueCountFrequency (%)
S 29
31.2%
C 11
 
11.8%
B 11
 
11.8%
N 8
 
8.6%
T 7
 
7.5%
Z 5
 
5.4%
J 4
 
4.3%
H 3
 
3.2%
I 3
 
3.2%
O 3
 
3.2%
Other values (5) 9
 
9.7%
Space Separator
ValueCountFrequency (%)
39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 365
89.5%
Common 43
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 46
 
12.6%
n 32
 
8.8%
S 29
 
7.9%
e 23
 
6.3%
u 21
 
5.8%
a 18
 
4.9%
h 18
 
4.9%
g 18
 
4.9%
s 14
 
3.8%
C 11
 
3.0%
Other values (25) 135
37.0%
Common
ValueCountFrequency (%)
39
90.7%
( 2
 
4.7%
) 2
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 46
 
11.3%
39
 
9.6%
n 32
 
7.8%
S 29
 
7.1%
e 23
 
5.6%
u 21
 
5.1%
a 18
 
4.4%
h 18
 
4.4%
g 18
 
4.4%
s 14
 
3.4%
Other values (28) 150
36.8%

BULD_YR
Real number (ℝ)

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.2245
Minimum1997
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:52.752615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2000
Q12011
median2013
Q32013
95-th percentile2014
Maximum2015
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.1796397
Coefficient of variation (CV)0.0020781567
Kurtosis5.2818533
Mean2011.2245
Median Absolute Deviation (MAD)1
Skewness-2.3958914
Sum98550
Variance17.469388
MonotonicityNot monotonic
2023-12-10T23:26:52.840120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2013 22
44.9%
2014 6
 
12.2%
2012 5
 
10.2%
2011 4
 
8.2%
2010 3
 
6.1%
1998 2
 
4.1%
2015 2
 
4.1%
2005 1
 
2.0%
2007 1
 
2.0%
2003 1
 
2.0%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
1997 1
 
2.0%
1998 2
 
4.1%
2003 1
 
2.0%
2005 1
 
2.0%
2007 1
 
2.0%
2008 1
 
2.0%
2010 3
 
6.1%
2011 4
 
8.2%
2012 5
 
10.2%
2013 22
44.9%
ValueCountFrequency (%)
2015 2
 
4.1%
2014 6
 
12.2%
2013 22
44.9%
2012 5
 
10.2%
2011 4
 
8.2%
2010 3
 
6.1%
2008 1
 
2.0%
2007 1
 
2.0%
2005 1
 
2.0%
2003 1
 
2.0%

DDWGHT
Real number (ℝ)

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89957.898
Minimum27780
Maximum206046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:52.946832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27780
5-th percentile34232.2
Q156520
median71663
Q3107236
95-th percentile181928.2
Maximum206046
Range178266
Interquartile range (IQR)50716

Descriptive statistics

Standard deviation54640.863
Coefficient of variation (CV)0.60740484
Kurtosis-0.46384536
Mean89957.898
Median Absolute Deviation (MAD)22750
Skewness0.99159284
Sum4407937
Variance2.9856239 × 109
MonotonicityNot monotonic
2023-12-10T23:26:53.053870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
40481 4
 
8.2%
63200 3
 
6.1%
179816 2
 
4.1%
58000 2
 
4.1%
179549 1
 
2.0%
56526 1
 
2.0%
75884 1
 
2.0%
32385 1
 
2.0%
182307 1
 
2.0%
206037 1
 
2.0%
Other values (32) 32
65.3%
ValueCountFrequency (%)
27780 1
 
2.0%
32203 1
 
2.0%
32385 1
 
2.0%
37003 1
 
2.0%
37009 1
 
2.0%
38980 1
 
2.0%
39017 1
 
2.0%
40481 4
8.2%
48913 1
 
2.0%
56520 1
 
2.0%
ValueCountFrequency (%)
206046 1
2.0%
206037 1
2.0%
182307 1
2.0%
181360 1
2.0%
179816 2
4.1%
179667 1
2.0%
179549 1
2.0%
176460 1
2.0%
176247 1
2.0%
175125 1
2.0%

DPTR_HMS
Date

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2022-01-01 00:00:49
Maximum2022-01-22 22:52:57
2023-12-10T23:26:53.166182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:53.289433image/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
2023-12-10T23:26:53.395635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:26:53.503704image/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%
Mean9.8325019
Minimum-38.784401
Maximum56.5882
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:26:53.616897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-38.784401
5-th percentile-34.49122
Q1-9.4755
median11.9299
Q330.0532
95-th percentile46.679821
Maximum56.5882
Range95.372601
Interquartile range (IQR)39.5287

Descriptive statistics

Standard deviation26.321211
Coefficient of variation (CV)2.6769597
Kurtosis-0.90994699
Mean9.8325019
Median Absolute Deviation (MAD)19.34777
Skewness-0.26211655
Sum481.79259
Variance692.80615
MonotonicityNot monotonic
2023-12-10T23:26:53.728426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
30.0532 1
 
2.0%
29.7561 1
 
2.0%
46.292702 1
 
2.0%
46.937901 1
 
2.0%
-10.0565 1
 
2.0%
-33.926701 1
 
2.0%
31.6064 1
 
2.0%
9.93235 1
 
2.0%
31.240801 1
 
2.0%
-20.385799 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%
-25.1632 1
2.0%
-21.6 1
2.0%
-20.385799 1
2.0%
-10.0801 1
2.0%
ValueCountFrequency (%)
56.5882 1
2.0%
50.9963 1
2.0%
46.937901 1
2.0%
46.292702 1
2.0%
45.6371 1
2.0%
43.891701 1
2.0%
37.4659 1
2.0%
36.248299 1
2.0%
33.356201 1
2.0%
32.318298 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.143151
Minimum-150.99699
Maximum166.248
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)30.6%
Memory size573.0 B
2023-12-10T23:26:53.829288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-150.99699
5-th percentile-114.09832
Q1-48.7533
median49.200199
Q3112.686
95-th percentile125.3252
Maximum166.248
Range317.245
Interquartile range (IQR)161.4393

Descriptive statistics

Standard deviation85.269537
Coefficient of variation (CV)2.8288196
Kurtosis-0.93377577
Mean30.143151
Median Absolute Deviation (MAD)65.234799
Skewness-0.44409822
Sum1477.0144
Variance7270.894
MonotonicityNot monotonic
2023-12-10T23:26:53.932983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-90.506302 1
 
2.0%
49.538502 1
 
2.0%
-124.991997 1
 
2.0%
32.020599 1
 
2.0%
72.727203 1
 
2.0%
113.658997 1
 
2.0%
31.7437 1
 
2.0%
-61.618 1
 
2.0%
122.036003 1
 
2.0%
116.57 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-150.996994 1
2.0%
-129.341995 1
2.0%
-124.991997 1
2.0%
-97.757797 1
2.0%
-90.506302 1
2.0%
-89.983498 1
2.0%
-81.235603 1
2.0%
-78.187698 1
2.0%
-73.481201 1
2.0%
-62.301102 1
2.0%
ValueCountFrequency (%)
166.248001 1
2.0%
160.514008 1
2.0%
127.517998 1
2.0%
122.036003 1
2.0%
121.632004 1
2.0%
119.727997 1
2.0%
116.57 1
2.0%
115.777 1
2.0%
115.720001 1
2.0%
115.138 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0330829
Minimum-35.942799
Maximum59.7024
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)42.9%
Memory size573.0 B
2023-12-10T23:26:54.073624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-35.942799
5-th percentile-34.110799
Q1-20.745001
median6.3484
Q327.661699
95-th percentile45.415681
Maximum59.7024
Range95.645199
Interquartile range (IQR)48.4067

Descriptive statistics

Standard deviation27.206512
Coefficient of variation (CV)5.4055363
Kurtosis-1.2199925
Mean5.0330829
Median Absolute Deviation (MAD)26.265999
Skewness0.049626682
Sum246.62106
Variance740.19432
MonotonicityNot monotonic
2023-12-10T23:26:54.195999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
20.876699 1
 
2.0%
-24.3557 1
 
2.0%
-23.976101 1
 
2.0%
-32.696201 1
 
2.0%
-28.962299 1
 
2.0%
-0.652322 1
 
2.0%
32.614399 1
 
2.0%
36.9907 1
 
2.0%
-20.0308 1
 
2.0%
-19.856701 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%
-33.810799 1
2.0%
-33.639599 1
2.0%
-32.696201 1
2.0%
-28.962299 1
2.0%
-24.3557 1
2.0%
-23.976101 1
2.0%
-23.846701 1
2.0%
ValueCountFrequency (%)
59.7024 1
2.0%
48.4459 1
2.0%
45.673801 1
2.0%
45.0285 1
2.0%
41.5284 1
2.0%
36.9907 1
2.0%
36.681301 1
2.0%
35.434299 1
2.0%
32.711601 1
2.0%
32.614399 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.919395
Minimum-172.168
Maximum164.107
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)32.7%
Memory size573.0 B
2023-12-10T23:26:54.306252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-172.168
5-th percentile-91.136441
Q1-35.895302
median36.675999
Q3116.225
95-th percentile150.506
Maximum164.107
Range336.27499
Interquartile range (IQR)152.1203

Descriptive statistics

Standard deviation86.866139
Coefficient of variation (CV)2.4183631
Kurtosis-0.70624731
Mean35.919395
Median Absolute Deviation (MAD)76.207004
Skewness-0.45999283
Sum1760.0504
Variance7545.726
MonotonicityNot monotonic
2023-12-10T23:26:54.425602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
72.595001 1
 
2.0%
-38.751301 1
 
2.0%
-46.287102 1
 
2.0%
-60.7229 1
 
2.0%
32.129101 1
 
2.0%
-8.46894 1
 
2.0%
127.727997 1
 
2.0%
126.724998 1
 
2.0%
118.529999 1
 
2.0%
118.538002 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-172.167999 1
2.0%
-152.457993 1
2.0%
-91.505203 1
2.0%
-90.583298 1
2.0%
-89.187401 1
2.0%
-72.620399 1
2.0%
-71.685501 1
2.0%
-60.7229 1
2.0%
-56.041 1
2.0%
-51.970299 1
2.0%
ValueCountFrequency (%)
164.106995 1
2.0%
151.460007 1
2.0%
151.278 1
2.0%
149.348007 1
2.0%
139.852997 1
2.0%
127.727997 1
2.0%
126.724998 1
2.0%
124.961998 1
2.0%
120.25 1
2.0%
118.538002 1
2.0%

NVGTN_TM
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4569.7071
Minimum3546.1
Maximum4725.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:54.565626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3546.1
5-th percentile3715.114
Q14694.92
median4723.12
Q34725.49
95-th percentile4725.898
Maximum4725.98
Range1179.88
Interquartile range (IQR)30.57

Descriptive statistics

Standard deviation346.52892
Coefficient of variation (CV)0.075831756
Kurtosis3.0523125
Mean4569.7071
Median Absolute Deviation (MAD)2.71
Skewness-2.1545745
Sum223915.65
Variance120082.29
MonotonicityNot monotonic
2023-12-10T23:26:54.693747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4724.07 1
 
2.0%
4706.94 1
 
2.0%
4725.18 1
 
2.0%
4725.49 1
 
2.0%
3719.08 1
 
2.0%
4725.7 1
 
2.0%
4702.63 1
 
2.0%
4725.16 1
 
2.0%
3546.1 1
 
2.0%
3719.85 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3546.1 1
2.0%
3700.79 1
2.0%
3712.47 1
2.0%
3719.08 1
2.0%
3719.23 1
2.0%
3719.85 1
2.0%
4198.38 1
2.0%
4376.08 1
2.0%
4567.53 1
2.0%
4594.44 1
2.0%
ValueCountFrequency (%)
4725.98 1
2.0%
4725.93 1
2.0%
4725.91 1
2.0%
4725.88 1
2.0%
4725.87 1
2.0%
4725.83 1
2.0%
4725.78 1
2.0%
4725.71 1
2.0%
4725.7 1
2.0%
4725.56 1
2.0%

PRT_NCHRG_TM
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0.0
48 
62.8508
 
1

Length

Max length7
Median length3
Mean length3.0816327
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
98.0%
62.8508 1
 
2.0%

Length

2023-12-10T23:26:54.823082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:26:54.924824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
98.0%
62.8508 1
 
2.0%

BLLAT_HOUR
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.8641
Minimum0.894444
Maximum927.979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:55.012509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.894444
5-th percentile13.478166
Q151.9142
median117.897
Q3302.775
95-th percentile569.1894
Maximum927.979
Range927.08456
Interquartile range (IQR)250.8608

Descriptive statistics

Standard deviation198.54213
Coefficient of variation (CV)0.96443298
Kurtosis2.5516908
Mean205.8641
Median Absolute Deviation (MAD)83.0028
Skewness1.5296648
Sum10087.341
Variance39418.976
MonotonicityNot monotonic
2023-12-10T23:26:55.117971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
320.878 1
 
2.0%
514.825 1
 
2.0%
18.87 1
 
2.0%
90.9003 1
 
2.0%
112.367 1
 
2.0%
424.893 1
 
2.0%
195.423 1
 
2.0%
66.5867 1
 
2.0%
0.894444 1
 
2.0%
199.198 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.894444 1
2.0%
5.25028 1
2.0%
9.88361 1
2.0%
18.87 1
2.0%
31.7078 1
2.0%
33.6622 1
2.0%
34.8942 1
2.0%
34.9967 1
2.0%
36.7647 1
2.0%
41.6019 1
2.0%
ValueCountFrequency (%)
927.979 1
2.0%
640.762 1
2.0%
570.883 1
2.0%
566.649 1
2.0%
514.825 1
2.0%
459.515 1
2.0%
458.31 1
2.0%
424.893 1
2.0%
370.156 1
2.0%
352.224 1
2.0%

FRGHT_CNVNC_TM
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4362.5604
Minimum3134.14
Maximum4714.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:26:55.438178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3134.14
5-th percentile3530.476
Q14263.19
median4499.51
Q34634.59
95-th percentile4692.672
Maximum4714.65
Range1580.51
Interquartile range (IQR)371.4

Descriptive statistics

Standard deviation383.93055
Coefficient of variation (CV)0.088005785
Kurtosis1.7171354
Mean4362.5604
Median Absolute Deviation (MAD)174.36
Skewness-1.5363213
Sum213765.46
Variance147402.67
MonotonicityNot monotonic
2023-12-10T23:26:55.544612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4403.19 1
 
2.0%
4192.12 1
 
2.0%
4706.31 1
 
2.0%
4634.59 1
 
2.0%
3606.71 1
 
2.0%
4300.81 1
 
2.0%
4507.2 1
 
2.0%
4658.57 1
 
2.0%
3545.2 1
 
2.0%
3520.66 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
3134.14 1
2.0%
3516.16 1
2.0%
3520.66 1
2.0%
3545.2 1
2.0%
3606.71 1
2.0%
3713.98 1
2.0%
3797.93 1
2.0%
4083.54 1
2.0%
4088.79 1
2.0%
4149.23 1
2.0%
ValueCountFrequency (%)
4714.65 1
2.0%
4706.31 1
2.0%
4694.12 1
2.0%
4690.5 1
2.0%
4688.78 1
2.0%
4685.5 1
2.0%
4682.95 1
2.0%
4681.82 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

2023-12-10T23:26:55.651009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:26:55.718538image/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
2023-12-10T23:26:55.811503image/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
2023-12-10T23:26:55.947200image/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
02292190009644201IZMIRBULK CARRIER32.0193.7418.5<NA>27.2114<NA>20136320001-Jan-2022 01:54:4817-Jul-2022 21:58:4830.0532-90.50630220.87669972.5950014724.070.0320.8784403.1902
12292200009657777SAMSUNBULK CARRIER32.26193.7418.5<NA>27.2114<NA>20136320001-Jan-2022 16:43:4517-Jul-2022 21:44:1719.93580163.21720114.3617-152.4579934709.010.0150.0034559.0103
22292210009644196SIIRTBULK CARRIER32.0193.7418.5<NA>27.2114<NA>20136320001-Jan-2022 00:58:5215-Jul-2022 22:22:5133.356201-48.45819930.053301-90.5832984677.40.0147.9594529.4404
32292360009315537FEDERAL MATTAWABULK CARRIER23.5176.014.1<NA>21.8201<NA>20052778001-Jan-2022 00:14:0216-Jul-2022 15:09:0345.6371-73.48120148.4459-89.1874014694.920.0117.8974577.0205
42292430009605499Flag SeamanBulk Carrier45.0282.024.8Golden Union17.5187SCS Shipbuilding201317646001-Jan-2022 00:13:0217-Jul-2022 20:31:0114.0239-150.99699419.331699112.8830034724.30.0640.7624083.5406
52292640009644500NBA RembrandtBulk Carrier43.0245.6219.39NYK Blkshp Atlnt30.0Oshima Shipbuilding201210723606-Jan-2022 11:24:1717-Jul-2022 21:50:441.34242104.36000135.434299139.8529974594.440.033.66224560.7807
62292650009644548LBC EarthChip Carrier36.5205.017.7NYK Blkshp Atlnt30.0Oshima Shipbuilding20127057801-Jan-2022 00:29:4417-Jul-2022 21:53:3056.58827.1485636.68130113.54494725.40.034.89424690.508
72292660009608702BIANCO OLIVIA BULKERBULK CARRIER28.4171.514.1<NA>25.2942<NA>20133220301-Jan-2022 00:01:5317-Jul-2022 20:35:02-33.96440125.76987.36167120.254724.550.041.60194682.9509
82292690009587245NegonegoBulk Carrier50.0294.024.9Cardiff Marine18.1539SCS Shipbuilding201320604601-Jan-2022 00:02:1617-Jul-2022 21:56:47-34.11579916.77249945.028536.6759994725.910.0927.9793797.93010
92292760009595979PEBBLE BEACHBULK CARRIER28.3183.015.2<NA>3.0<NA>20133700322-Jan-2022 22:52:5717-Jul-2022 21:15:4425.936899121.63200422.6661-91.5052034198.380.0109.5914088.79011
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
392294620009323900ELEOUSSABULK CARRIER32.26185.018.1<NA>24.4033<NA>20085667801-Jan-2022 05:03:1117-Jul-2022 21:33:1737.46594.17491-19.9338-26.5491014720.50.034.99674685.5041
402294780009650171KIRAN ANATOLIABULK CARRIER32.26194.518.5<NA>27.3311<NA>20136347801-Jan-2022 00:09:0517-Jul-2022 21:35:08-1.51176-48.753332.711601-29.7007014725.430.0352.2244373.21042
412294860009643910IOANNIS GBULK CARRIER32.26185.018.0<NA>24.3353<NA>20115652001-Jan-2022 00:05:4717-Jul-2022 17:44:2919.308190.45760331.137129.7957994721.650.0370.1564351.49043
422295230009668403GIORGOS DRACOPOULOSBULK CARRIER32.24199.9818.6<NA>4.0<NA>20106139801-Jan-2022 00:09:5017-Jul-2022 21:59:2110.128387.4383013.3700176.2429964725.830.031.70784694.12044
432295290009618006LA BRIANTAISBULK CARRIER30.0176.6515.0<NA>21.0<NA>20134048101-Jan-2022 00:17:3017-Jul-2022 21:59:58-21.6166.248001-21.019699164.1069954725.710.0306.7324418.98045
442295300009618018LA GUIMORAISBULK CARRIER30.0176.6515.0<NA>21.0<NA>20144048101-Jan-2022 04:03:2817-Jul-2022 21:57:0618.0217115.138-2.50167151.2784721.890.0107.8894614.0046
452295310009617519Thalassini AstridBulk Carrier45.0286.6624.8Enesel SA17.5907Tianjin Xingang HI201417981601-Jan-2022 00:24:0715-Jul-2022 19:44:2611.2617127.51799821.536699108.3624675.3462.8508302.3214310.17047
462295320009617521GH KahloBulk Carrier45.0286.6624.8Greenheart Mngt17.5907Tianjin Xingang HI201417981601-Jan-2022 01:48:1517-Jul-2022 21:55:0624.92670154.540001-33.81079918.1319014724.110.0224.6064499.51048
472295330009646900LA RICHARDAISBULK CARRIER30.0176.6515.0<NA>30.0<NA>20144048101-Jan-2022 00:12:0017-Jul-2022 21:44:34-25.1632160.51400827.661699124.9619984725.540.069.06254656.48049
482295340009646895LA LANDRIAISBULK CARRIER30.0176.6515.0<NA>30.0<NA>20144048101-Jan-2022 04:10:1817-Jul-2022 21:40:1232.318298119.72799745.673801-172.1679994721.50.0458.314263.19050