Overview

Dataset statistics

Number of variables37
Number of observations49
Missing cells80
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.8 KiB
Average record size in memory329.7 B

Variable types

Numeric31
Text3
Categorical3

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001054

Alerts

DPTR_HMS is highly imbalanced (56.0%)Imbalance
SHIP_OWNER_NM has 40 (81.6%) missing valuesMissing
SHPYRD_NM has 40 (81.6%) missing valuesMissing
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM has unique valuesUnique
DPTRP_LA has unique valuesUnique
DPTRP_LO has unique valuesUnique
DTNT_LA has unique valuesUnique
DTNT_LO has unique valuesUnique
AVE_VE has unique valuesUnique
MAX_VE has unique valuesUnique
NVGTN_DIST has unique valuesUnique
WAVE_AVE_CYCL has unique valuesUnique
WAVE_AVE_HGHT has unique valuesUnique
AVE_WDSP has unique valuesUnique
ADDTI_RSTC has unique valuesUnique
TOT_RSTC has unique valuesUnique
RL_POWER has unique valuesUnique
FUEL_CNSMP_QTY has unique valuesUnique
CDBX has unique valuesUnique
NOX has unique valuesUnique
SOX has unique valuesUnique
MTHN has unique valuesUnique
SHIP_NRG_EFFCN_NVGTN_IDX has unique valuesUnique
RN has unique valuesUnique
MMSI has 2 (4.1%) zerosZeros
IMO_IDNTF_NO has 1 (2.0%) zerosZeros
AVE_VE has 1 (2.0%) zerosZeros
MAX_VE has 1 (2.0%) zerosZeros
NVGTN_DIST has 1 (2.0%) zerosZeros
ADDTI_RSTC has 1 (2.0%) zerosZeros
TOT_RSTC has 1 (2.0%) zerosZeros
FUEL_CNSMP_QTY has 1 (2.0%) zerosZeros
CDBX has 1 (2.0%) zerosZeros
SOX has 1 (2.0%) zerosZeros
SHIP_NRG_EFFCN_NVGTN_IDX has 1 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:32:15.905615
Analysis finished2023-12-10 14:32:16.279914
Duration0.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MMSI
Real number (ℝ)

ZEROS 

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8244135 × 108
Minimum0
Maximum6.3601677 × 108
Zeros2
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:16.361098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.3200665 × 108
Q12.5703714 × 108
median2.6561139 × 108
Q32.710404 × 108
95-th percentile5.3800558 × 108
Maximum6.3601677 × 108
Range6.3601677 × 108
Interquartile range (IQR)14003265

Descriptive statistics

Standard deviation1.0751098 × 108
Coefficient of variation (CV)0.38064888
Kurtosis4.3484546
Mean2.8244135 × 108
Median Absolute Deviation (MAD)8539770
Skewness0.98628401
Sum1.3839626 × 1010
Variance1.1558611 × 1016
MonotonicityNot monotonic
2023-12-10T23:32:16.533891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 2
 
4.1%
232006651 1
 
2.0%
257107350 1
 
2.0%
257112270 1
 
2.0%
265519440 1
 
2.0%
265611390 1
 
2.0%
271000642 1
 
2.0%
271000845 1
 
2.0%
271000874 1
 
2.0%
271000973 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
0 2
4.1%
232006648 1
2.0%
232006651 1
2.0%
232008101 1
2.0%
232008102 1
2.0%
232008104 1
2.0%
255806472 1
2.0%
255806473 1
2.0%
255806474 1
2.0%
255806475 1
2.0%
ValueCountFrequency (%)
636016774 1
2.0%
538006002 1
2.0%
538006001 1
2.0%
538004953 1
2.0%
477786900 1
2.0%
354954000 1
2.0%
351463000 1
2.0%
271042567 1
2.0%
271042566 1
2.0%
271042389 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9267092.4
Minimum0
Maximum9884667
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:16.669133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7809947.2
Q19414917
median9663740
Q39764491
95-th percentile9858662.4
Maximum9884667
Range9884667
Interquartile range (IQR)349574

Descriptive statistics

Standard deviation1539432.9
Coefficient of variation (CV)0.16611822
Kurtosis28.890258
Mean9267092.4
Median Absolute Deviation (MAD)164483
Skewness-5.1365574
Sum4.5408753 × 108
Variance2.3698536 × 1012
MonotonicityNot monotonic
2023-12-10T23:32:16.818551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9719240 1
 
2.0%
0 1
 
2.0%
9836361 1
 
2.0%
9866782 1
 
2.0%
9828223 1
 
2.0%
7006194 1
 
2.0%
5372965 1
 
2.0%
9015577 1
 
2.0%
9040895 1
 
2.0%
9254472 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0 1
2.0%
5372965 1
2.0%
7006194 1
2.0%
9015577 1
2.0%
9040895 1
2.0%
9181728 1
2.0%
9224673 1
2.0%
9245249 1
2.0%
9254472 1
2.0%
9291406 1
2.0%
ValueCountFrequency (%)
9884667 1
2.0%
9884655 1
2.0%
9866782 1
2.0%
9846483 1
2.0%
9836361 1
2.0%
9829796 1
2.0%
9829784 1
2.0%
9828223 1
2.0%
9808261 1
2.0%
9808259 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:32:17.049421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length10.122449
Min length5

Characters and Unicode

Total characters496
Distinct characters47
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

Unique49 ?
Unique (%)100.0%

Sample

1st rowSTOLT APAL
2nd rowSTOLT LIND
3rd rowSTOLT EBONY
4th rowSTOLT MAPLE
5th rowSTOLT PALM
ValueCountFrequency (%)
stolt 5
 
6.0%
ince 3
 
3.6%
ulusoy 3
 
3.6%
idc 3
 
3.6%
bow 2
 
2.4%
kiran 2
 
2.4%
sbi 2
 
2.4%
excellence 1
 
1.2%
jehander 1
 
1.2%
1 1
 
1.2%
Other values (61) 61
72.6%
2023-12-10T23:32:17.431540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 39
 
7.9%
35
 
7.1%
E 34
 
6.9%
I 32
 
6.5%
O 29
 
5.8%
L 28
 
5.6%
S 27
 
5.4%
R 26
 
5.2%
T 23
 
4.6%
N 23
 
4.6%
Other values (37) 200
40.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 389
78.4%
Lowercase Letter 58
 
11.7%
Space Separator 35
 
7.1%
Decimal Number 11
 
2.2%
Dash Punctuation 3
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 39
 
10.0%
E 34
 
8.7%
I 32
 
8.2%
O 29
 
7.5%
L 28
 
7.2%
S 27
 
6.9%
R 26
 
6.7%
T 23
 
5.9%
N 23
 
5.9%
U 18
 
4.6%
Other values (15) 110
28.3%
Lowercase Letter
ValueCountFrequency (%)
a 8
13.8%
o 8
13.8%
n 7
12.1%
s 5
8.6%
l 4
6.9%
d 4
6.9%
r 4
6.9%
e 4
6.9%
f 3
 
5.2%
y 3
 
5.2%
Other values (5) 8
13.8%
Decimal Number
ValueCountFrequency (%)
1 4
36.4%
2 3
27.3%
9 2
18.2%
0 1
 
9.1%
8 1
 
9.1%
Space Separator
ValueCountFrequency (%)
35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 447
90.1%
Common 49
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 39
 
8.7%
E 34
 
7.6%
I 32
 
7.2%
O 29
 
6.5%
L 28
 
6.3%
S 27
 
6.0%
R 26
 
5.8%
T 23
 
5.1%
N 23
 
5.1%
U 18
 
4.0%
Other values (30) 168
37.6%
Common
ValueCountFrequency (%)
35
71.4%
1 4
 
8.2%
- 3
 
6.1%
2 3
 
6.1%
9 2
 
4.1%
0 1
 
2.0%
8 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 39
 
7.9%
35
 
7.1%
E 34
 
6.9%
I 32
 
6.5%
O 29
 
5.8%
L 28
 
5.6%
S 27
 
5.4%
R 26
 
5.2%
T 23
 
4.6%
N 23
 
4.6%
Other values (37) 200
40.3%

SHIP_KIND
Categorical

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
BULK CARRIER
15 
Bulk Carrier
14 
Chemical/Oil Product
13 
Cement Carrier
2 
Aggregates Carrier
2 
Other values (3)
3 

Length

Max length27
Median length12
Mean length14.816327
Min length12

Unique

Unique3 ?
Unique (%)6.1%

Sample

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

Common Values

ValueCountFrequency (%)
BULK CARRIER 15
30.6%
Bulk Carrier 14
28.6%
Chemical/Oil Product 13
26.5%
Cement Carrier 2
 
4.1%
Aggregates Carrier 2
 
4.1%
Bulk & Caustic Soda Carrier 1
 
2.0%
GENERAL CARGO 1
 
2.0%
Anti-Pollution 1
 
2.0%

Length

2023-12-10T23:32:17.582701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:17.691221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
carrier 34
34.0%
bulk 30
30.0%
chemical/oil 13
 
13.0%
product 13
 
13.0%
cement 2
 
2.0%
aggregates 2
 
2.0%
1
 
1.0%
caustic 1
 
1.0%
soda 1
 
1.0%
general 1
 
1.0%
Other values (2) 2
 
2.0%

SHIP_WDTH
Real number (ℝ)

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.13298
Minimum6.91
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:17.821102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.91
5-th percentile9.252
Q124.8
median29.8
Q332.26
95-th percentile32.3
Maximum33
Range26.09
Interquartile range (IQR)7.46

Descriptive statistics

Standard deviation7.2208544
Coefficient of variation (CV)0.26612832
Kurtosis1.6740921
Mean27.13298
Median Absolute Deviation (MAD)2.46
Skewness-1.5878033
Sum1329.516
Variance52.140738
MonotonicityNot monotonic
2023-12-10T23:32:17.927434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
32.26 13
26.5%
28.0 5
 
10.2%
15.603 2
 
4.1%
32.3 2
 
4.1%
29.0 1
 
2.0%
8.0 1
 
2.0%
6.91 1
 
2.0%
24.8 1
 
2.0%
32.24 1
 
2.0%
24.0 1
 
2.0%
Other values (21) 21
42.9%
ValueCountFrequency (%)
6.91 1
2.0%
8.0 1
2.0%
8.62 1
2.0%
10.2 1
2.0%
15.603 2
4.1%
19.67 1
2.0%
19.68 1
2.0%
20.8 1
2.0%
23.87 1
2.0%
23.88 1
2.0%
ValueCountFrequency (%)
33.0 1
 
2.0%
32.31 1
 
2.0%
32.3 2
 
4.1%
32.29 1
 
2.0%
32.27 1
 
2.0%
32.26 13
26.5%
32.25 1
 
2.0%
32.24 1
 
2.0%
32.2 1
 
2.0%
32.04 1
 
2.0%

SHIP_LNTH
Real number (ℝ)

Distinct34
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.95438
Minimum37.77
Maximum228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:18.019061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.77
5-th percentile67.284
Q1153.96
median182.8
Q3189.99
95-th percentile225.5
Maximum228
Range190.23
Interquartile range (IQR)36.03

Descriptive statistics

Standard deviation46.924976
Coefficient of variation (CV)0.27610337
Kurtosis1.3788465
Mean169.95438
Median Absolute Deviation (MAD)14.2
Skewness-1.276326
Sum8327.7644
Variance2201.9533
MonotonicityNot monotonic
2023-12-10T23:32:18.121637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
185.0 7
 
14.3%
225.5 4
 
8.2%
182.0 3
 
6.1%
222.0 2
 
4.1%
183.0 2
 
4.1%
90.3772 2
 
4.1%
197.0 2
 
4.1%
62.0 1
 
2.0%
171.6 1
 
2.0%
228.0 1
 
2.0%
Other values (24) 24
49.0%
ValueCountFrequency (%)
37.77 1
2.0%
44.7 1
2.0%
62.0 1
2.0%
75.21 1
2.0%
90.3772 2
4.1%
125.4 1
2.0%
129.4 1
2.0%
129.44 1
2.0%
149.5 1
2.0%
149.8 1
2.0%
ValueCountFrequency (%)
228.0 1
 
2.0%
225.5 4
8.2%
224.85 1
 
2.0%
222.0 2
 
4.1%
220.18 1
 
2.0%
197.0 2
 
4.1%
196.13 1
 
2.0%
189.99 1
 
2.0%
188.5 1
 
2.0%
185.0 7
14.3%

SHIP_HGHT
Real number (ℝ)

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.380871
Minimum4.61792
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:18.229061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.61792
5-th percentile5.450102
Q113.6
median18
Q320.25
95-th percentile50
Maximum50
Range45.38208
Interquartile range (IQR)6.65

Descriptive statistics

Standard deviation15.884638
Coefficient of variation (CV)0.67938605
Kurtosis-0.68918641
Mean23.380871
Median Absolute Deviation (MAD)4.4
Skewness0.96180678
Sum1145.6627
Variance252.32172
MonotonicityNot monotonic
2023-12-10T23:32:18.326650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
50.0 12
24.5%
20.05 4
 
8.2%
18.6 2
 
4.1%
11.1419 2
 
4.1%
20.25 2
 
4.1%
13.6 2
 
4.1%
7.56665 2
 
4.1%
15.0 2
 
4.1%
13.25 1
 
2.0%
14.2 1
 
2.0%
Other values (19) 19
38.8%
ValueCountFrequency (%)
4.61792 1
2.0%
4.93118 1
2.0%
5.42831 1
2.0%
5.48279 1
2.0%
7.56665 2
4.1%
11.0 1
2.0%
11.1419 2
4.1%
12.2233 1
2.0%
12.5321 1
2.0%
13.25 1
2.0%
ValueCountFrequency (%)
50.0 12
24.5%
20.25 2
 
4.1%
20.19 1
 
2.0%
20.05 4
 
8.2%
19.39 1
 
2.0%
18.6 2
 
4.1%
18.3 1
 
2.0%
18.1 1
 
2.0%
18.0 1
 
2.0%
17.8 1
 
2.0%

SHIP_OWNER_NM
Text

MISSING 

Distinct5
Distinct (%)55.6%
Missing40
Missing (%)81.6%
Memory size524.0 B
2023-12-10T23:32:18.445521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length14.222222
Min length6

Characters and Unicode

Total characters128
Distinct characters27
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

Unique3 ?
Unique (%)33.3%

Sample

1st rowTrust Bulkers
2nd rowTrust Bulkers
3rd rowTrust Bulkers
4th rowTrust Bulkers
5th rowOldendorff Carriers
ValueCountFrequency (%)
trust 4
22.2%
bulkers 4
22.2%
ulusoy 2
11.1%
denizyollari 2
11.1%
oldendorff 1
 
5.6%
carriers 1
 
5.6%
kcc 1
 
5.6%
as 1
 
5.6%
kiran 1
 
5.6%
holding 1
 
5.6%
2023-12-10T23:32:18.664257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 15
 
11.7%
l 12
 
9.4%
s 11
 
8.6%
u 10
 
7.8%
9
 
7.0%
e 8
 
6.2%
i 7
 
5.5%
o 6
 
4.7%
n 5
 
3.9%
a 4
 
3.1%
Other values (17) 41
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98
76.6%
Uppercase Letter 21
 
16.4%
Space Separator 9
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 15
15.3%
l 12
12.2%
s 11
11.2%
u 10
10.2%
e 8
8.2%
i 7
7.1%
o 6
 
6.1%
n 5
 
5.1%
a 4
 
4.1%
y 4
 
4.1%
Other values (6) 16
16.3%
Uppercase Letter
ValueCountFrequency (%)
T 4
19.0%
B 4
19.0%
C 3
14.3%
K 2
9.5%
D 2
9.5%
U 2
9.5%
O 1
 
4.8%
A 1
 
4.8%
S 1
 
4.8%
H 1
 
4.8%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119
93.0%
Common 9
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 15
12.6%
l 12
 
10.1%
s 11
 
9.2%
u 10
 
8.4%
e 8
 
6.7%
i 7
 
5.9%
o 6
 
5.0%
n 5
 
4.2%
a 4
 
3.4%
y 4
 
3.4%
Other values (16) 37
31.1%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 15
 
11.7%
l 12
 
9.4%
s 11
 
8.6%
u 10
 
7.8%
9
 
7.0%
e 8
 
6.2%
i 7
 
5.5%
o 6
 
4.7%
n 5
 
3.9%
a 4
 
3.1%
Other values (17) 41
32.0%

DRAFT
Real number (ℝ)

Distinct32
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.170438
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:18.762111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.8
Q112.8637
median25.7827
Q330
95-th percentile30
Maximum30
Range28
Interquartile range (IQR)17.1363

Descriptive statistics

Standard deviation9.4052055
Coefficient of variation (CV)0.42422282
Kurtosis-0.70191552
Mean22.170438
Median Absolute Deviation (MAD)4.2173
Skewness-0.94114821
Sum1086.3514
Variance88.457891
MonotonicityNot monotonic
2023-12-10T23:32:18.856575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
30.0 15
30.6%
18.4407 2
 
4.1%
23.7994 2
 
4.1%
2.0 2
 
4.1%
7.61991 1
 
2.0%
7.56213 1
 
2.0%
23.0375 1
 
2.0%
28.7752 1
 
2.0%
8.16645 1
 
2.0%
12.6437 1
 
2.0%
Other values (22) 22
44.9%
ValueCountFrequency (%)
2.0 2
4.1%
5.0 1
2.0%
7.0 1
2.0%
7.56213 1
2.0%
7.61991 1
2.0%
7.97278 1
2.0%
8.03993 1
2.0%
8.16645 1
2.0%
8.29014 1
2.0%
10.1472 1
2.0%
ValueCountFrequency (%)
30.0 15
30.6%
29.9182 1
 
2.0%
29.8475 1
 
2.0%
28.9395 1
 
2.0%
28.7752 1
 
2.0%
28.2766 1
 
2.0%
28.2106 1
 
2.0%
27.5507 1
 
2.0%
26.5285 1
 
2.0%
26.5048 1
 
2.0%

SHPYRD_NM
Text

MISSING 

Distinct5
Distinct (%)55.6%
Missing40
Missing (%)81.6%
Memory size524.0 B
2023-12-10T23:32:18.983753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length15.555556
Min length14

Characters and Unicode

Total characters140
Distinct characters28
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

Unique3 ?
Unique (%)33.3%

Sample

1st rowHudong Zhonghua
2nd rowHudong Zhonghua
3rd rowHudong Zhonghua
4th rowHudong Zhonghua
5th rowOshima Shipbuilding
ValueCountFrequency (%)
hudong 4
20.0%
zhonghua 4
20.0%
jiangsu 2
10.0%
eastern 2
10.0%
oshima 1
 
5.0%
shipbuilding 1
 
5.0%
zhejiang 1
 
5.0%
ouhua 1
 
5.0%
sb 1
 
5.0%
2 1
 
5.0%
Other values (2) 2
10.0%
2023-12-10T23:32:19.218032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 14
 
10.0%
h 13
 
9.3%
u 13
 
9.3%
a 13
 
9.3%
g 12
 
8.6%
11
 
7.9%
i 9
 
6.4%
o 8
 
5.7%
d 6
 
4.3%
Z 5
 
3.6%
Other values (18) 36
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 108
77.1%
Uppercase Letter 20
 
14.3%
Space Separator 11
 
7.9%
Decimal Number 1
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 14
13.0%
h 13
12.0%
u 13
12.0%
a 13
12.0%
g 12
11.1%
i 9
8.3%
o 8
7.4%
d 6
5.6%
s 5
 
4.6%
e 3
 
2.8%
Other values (8) 12
11.1%
Uppercase Letter
ValueCountFrequency (%)
Z 5
25.0%
H 4
20.0%
S 3
15.0%
O 2
 
10.0%
E 2
 
10.0%
J 2
 
10.0%
B 1
 
5.0%
M 1
 
5.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 128
91.4%
Common 12
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 14
10.9%
h 13
10.2%
u 13
10.2%
a 13
10.2%
g 12
 
9.4%
i 9
 
7.0%
o 8
 
6.2%
d 6
 
4.7%
Z 5
 
3.9%
s 5
 
3.9%
Other values (16) 30
23.4%
Common
ValueCountFrequency (%)
11
91.7%
2 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 14
 
10.0%
h 13
 
9.3%
u 13
 
9.3%
a 13
 
9.3%
g 12
 
8.6%
11
 
7.9%
i 9
 
6.4%
o 8
 
5.7%
d 6
 
4.3%
Z 5
 
3.6%
Other values (18) 36
25.7%

BULD_YR
Real number (ℝ)

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.7347
Minimum1946
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:19.316873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1946
5-th percentile1999.2
Q12008
median2015
Q32017
95-th percentile2020.6
Maximum2022
Range76
Interquartile range (IQR)9

Descriptive statistics

Standard deviation12.76025
Coefficient of variation (CV)0.0063460635
Kurtosis15.025615
Mean2010.7347
Median Absolute Deviation (MAD)4
Skewness-3.4913976
Sum98526
Variance162.82398
MonotonicityNot monotonic
2023-12-10T23:32:19.419067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2017 6
12.2%
2016 5
 
10.2%
2018 5
 
10.2%
2010 4
 
8.2%
2011 3
 
6.1%
2015 3
 
6.1%
2008 3
 
6.1%
2002 2
 
4.1%
2005 2
 
4.1%
2001 2
 
4.1%
Other values (11) 14
28.6%
ValueCountFrequency (%)
1946 1
 
2.0%
1970 1
 
2.0%
1998 1
 
2.0%
2001 2
4.1%
2002 2
4.1%
2005 2
4.1%
2006 1
 
2.0%
2008 3
6.1%
2009 1
 
2.0%
2010 4
8.2%
ValueCountFrequency (%)
2022 1
 
2.0%
2021 2
 
4.1%
2020 2
 
4.1%
2019 2
 
4.1%
2018 5
10.2%
2017 6
12.2%
2016 5
10.2%
2015 3
6.1%
2013 1
 
2.0%
2012 1
 
2.0%

DDWGHT
Real number (ℝ)

Distinct47
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41241.306
Minimum320
Maximum81600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:19.574581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum320
5-th percentile1542
Q124668
median38000
Q356925
95-th percentile81235
Maximum81600
Range81280
Interquartile range (IQR)32257

Descriptive statistics

Standard deviation24981.33
Coefficient of variation (CV)0.60573566
Kurtosis-0.93419489
Mean41241.306
Median Absolute Deviation (MAD)18116
Skewness0.07177424
Sum2020824
Variance6.2406684 × 108
MonotonicityNot monotonic
2023-12-10T23:32:19.724012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4650 2
 
4.1%
9900 2
 
4.1%
32798 1
 
2.0%
38000 1
 
2.0%
1590 1
 
2.0%
320 1
 
2.0%
29330 1
 
2.0%
66832 1
 
2.0%
52376 1
 
2.0%
22303 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
320 1
2.0%
780 1
2.0%
1510 1
2.0%
1590 1
2.0%
4650 2
4.1%
9900 2
4.1%
11853 1
2.0%
18600 1
2.0%
19884 1
2.0%
22303 1
2.0%
ValueCountFrequency (%)
81600 1
2.0%
81252 1
2.0%
81237 1
2.0%
81232 1
2.0%
80344 1
2.0%
79422 1
2.0%
79403 1
2.0%
77171 1
2.0%
66832 1
2.0%
63988 1
2.0%

DPTR_HMS
Categorical

IMBALANCE 

Distinct10
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2022 12:00:00
38 
01-Jan-2022 18:00:00
 
2
01-Jan-2022 06:00:00
 
2
02-Jan-2022 00:00:00
 
1
20-Mar-2022 12:00:00
 
1
Other values (5)
5 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique7 ?
Unique (%)14.3%

Sample

1st row01-Jan-2022 12:00:00
2nd row01-Jan-2022 12:00:00
3rd row01-Jan-2022 18:00:00
4th row01-Jan-2022 12:00:00
5th row01-Jan-2022 12:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2022 12:00:00 38
77.6%
01-Jan-2022 18:00:00 2
 
4.1%
01-Jan-2022 06:00:00 2
 
4.1%
02-Jan-2022 00:00:00 1
 
2.0%
20-Mar-2022 12:00:00 1
 
2.0%
02-Jan-2022 06:00:00 1
 
2.0%
03-Jan-2022 12:00:00 1
 
2.0%
08-Jan-2022 00:00:00 1
 
2.0%
03-Jan-2022 06:00:00 1
 
2.0%
16-May-2022 18:00:00 1
 
2.0%

Length

2023-12-10T23:32:19.853488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:32:19.975482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
01-jan-2022 42
42.9%
12:00:00 40
40.8%
06:00:00 4
 
4.1%
18:00:00 3
 
3.1%
02-jan-2022 2
 
2.0%
00:00:00 2
 
2.0%
03-jan-2022 2
 
2.0%
20-mar-2022 1
 
1.0%
08-jan-2022 1
 
1.0%
16-may-2022 1
 
1.0%

ARVL_HMS
Categorical

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
17-Jul-2022 18:00:00
20 
05-Jun-2022 18:00:00
15 
17-Jul-2022 12:00:00
5 
16-Jul-2022 18:00:00
 
2
05-Jun-2022 12:00:00
 
1
Other values (6)
6 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique7 ?
Unique (%)14.3%

Sample

1st row17-Jul-2022 18:00:00
2nd row17-Jul-2022 18:00:00
3rd row17-Jul-2022 12:00:00
4th row17-Jul-2022 18:00:00
5th row17-Jul-2022 18:00:00

Common Values

ValueCountFrequency (%)
17-Jul-2022 18:00:00 20
40.8%
05-Jun-2022 18:00:00 15
30.6%
17-Jul-2022 12:00:00 5
 
10.2%
16-Jul-2022 18:00:00 2
 
4.1%
05-Jun-2022 12:00:00 1
 
2.0%
04-Jun-2022 18:00:00 1
 
2.0%
17-Jul-2022 06:00:00 1
 
2.0%
17-Jul-2022 00:00:00 1
 
2.0%
15-Jul-2022 12:00:00 1
 
2.0%
14-Jul-2022 06:00:00 1
 
2.0%

Length

2023-12-10T23:32:20.114534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18:00:00 39
39.8%
17-jul-2022 27
27.6%
05-jun-2022 16
16.3%
12:00:00 7
 
7.1%
16-jul-2022 2
 
2.0%
06:00:00 2
 
2.0%
04-jun-2022 1
 
1.0%
00:00:00 1
 
1.0%
15-jul-2022 1
 
1.0%
14-jul-2022 1
 
1.0%

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.782205
Minimum-36.591801
Maximum63.054798
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)8.2%
Memory size573.0 B
2023-12-10T23:32:20.226425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-36.591801
5-th percentile-22.65956
Q122.4188
median34.245701
Q343.2001
95-th percentile59.953301
Maximum63.054798
Range99.646599
Interquartile range (IQR)20.7813

Descriptive statistics

Standard deviation23.437205
Coefficient of variation (CV)0.76138812
Kurtosis1.6589402
Mean30.782205
Median Absolute Deviation (MAD)11.760201
Skewness-1.2105076
Sum1508.3281
Variance549.3026
MonotonicityNot monotonic
2023-12-10T23:32:20.349441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
36.067101 1
 
2.0%
40.8745 1
 
2.0%
22.4188 1
 
2.0%
28.865101 1
 
2.0%
18.4289 1
 
2.0%
59.303902 1
 
2.0%
59.310902 1
 
2.0%
40.353802 1
 
2.0%
1.91955 1
 
2.0%
21.065201 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-36.591801 1
2.0%
-32.185398 1
2.0%
-31.0284 1
2.0%
-10.1063 1
2.0%
1.24802 1
2.0%
1.91955 1
2.0%
3.9968 1
2.0%
9.99248 1
2.0%
18.4289 1
2.0%
21.065201 1
2.0%
ValueCountFrequency (%)
63.054798 1
2.0%
61.008499 1
2.0%
60.004902 1
2.0%
59.8759 1
2.0%
59.310902 1
2.0%
59.303902 1
2.0%
55.722 1
2.0%
53.322701 1
2.0%
52.914501 1
2.0%
52.457298 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.262524
Minimum-97.871399
Maximum129.746
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)30.6%
Memory size573.0 B
2023-12-10T23:32:20.462872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-97.871399
5-th percentile-86.304803
Q1-5.95279
median21.1045
Q337.9506
95-th percentile123.0892
Maximum129.746
Range227.6174
Interquartile range (IQR)43.90339

Descriptive statistics

Standard deviation57.169945
Coefficient of variation (CV)2.8214622
Kurtosis-0.03142219
Mean20.262524
Median Absolute Deviation (MAD)27.05729
Skewness-0.076832605
Sum992.86368
Variance3268.4027
MonotonicityNot monotonic
2023-12-10T23:32:20.578934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-6.95839 1
 
2.0%
29.214199 1
 
2.0%
63.8414 1
 
2.0%
-95.362999 1
 
2.0%
64.302902 1
 
2.0%
18.084299 1
 
2.0%
18.1007 1
 
2.0%
27.9594 1
 
2.0%
104.111 1
 
2.0%
69.396599 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-97.871399 1
2.0%
-95.362999 1
2.0%
-93.102203 1
2.0%
-76.108704 1
2.0%
-69.896698 1
2.0%
-65.281403 1
2.0%
-44.590302 1
2.0%
-26.702 1
2.0%
-23.362101 1
2.0%
-22.447901 1
2.0%
ValueCountFrequency (%)
129.746002 1
2.0%
126.208 1
2.0%
124.244003 1
2.0%
121.357002 1
2.0%
104.111 1
2.0%
103.882004 1
2.0%
91.7174 1
2.0%
91.601997 1
2.0%
69.396599 1
2.0%
64.302902 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.394161
Minimum-35.121799
Maximum62.583401
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)22.4%
Memory size573.0 B
2023-12-10T23:32:20.954846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-35.121799
5-th percentile-30.389301
Q15.99049
median31.3675
Q347.377899
95-th percentile59.359042
Maximum62.583401
Range97.7052
Interquartile range (IQR)41.387409

Descriptive statistics

Standard deviation29.198936
Coefficient of variation (CV)1.1969641
Kurtosis-0.71761853
Mean24.394161
Median Absolute Deviation (MAD)22.682202
Skewness-0.64706973
Sum1195.3139
Variance852.57784
MonotonicityNot monotonic
2023-12-10T23:32:21.074147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5.99049 1
 
2.0%
40.9869 1
 
2.0%
-12.0521 1
 
2.0%
23.1745 1
 
2.0%
29.390301 1
 
2.0%
59.303902 1
 
2.0%
59.395802 1
 
2.0%
40.848099 1
 
2.0%
31.3675 1
 
2.0%
40.9576 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-35.121799 1
2.0%
-34.857899 1
2.0%
-33.218102 1
2.0%
-26.146099 1
2.0%
-24.193199 1
2.0%
-23.6737 1
2.0%
-18.1535 1
2.0%
-12.0521 1
2.0%
-9.22351 1
2.0%
-6.82575 1
2.0%
ValueCountFrequency (%)
62.583401 1
2.0%
59.734901 1
2.0%
59.395802 1
2.0%
59.303902 1
2.0%
58.623501 1
2.0%
57.925598 1
2.0%
57.224499 1
2.0%
55.830002 1
2.0%
54.049702 1
2.0%
53.100399 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.114455
Minimum-123.221
Maximum141.067
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)26.5%
Memory size573.0 B
2023-12-10T23:32:21.190468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-123.221
5-th percentile-74.457843
Q1-5.3931
median17.8405
Q350.667999
95-th percentile120.901
Maximum141.067
Range264.288
Interquartile range (IQR)56.061099

Descriptive statistics

Standard deviation62.306201
Coefficient of variation (CV)2.6955514
Kurtosis-0.31144872
Mean23.114455
Median Absolute Deviation (MAD)31.585899
Skewness-0.035037376
Sum1132.6083
Variance3882.0627
MonotonicityNot monotonic
2023-12-10T23:32:21.317575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
79.146103 1
 
2.0%
28.9639 1
 
2.0%
-77.145203 1
 
2.0%
119.689003 1
 
2.0%
-94.773697 1
 
2.0%
18.083599 1
 
2.0%
17.8405 1
 
2.0%
29.2668 1
 
2.0%
121.709 1
 
2.0%
28.773899 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-123.221001 1
2.0%
-94.773697 1
2.0%
-77.145203 1
2.0%
-70.426804 1
2.0%
-57.370399 1
2.0%
-55.3633 1
2.0%
-48.7808 1
2.0%
-47.536701 1
2.0%
-46.283001 1
2.0%
-43.006901 1
2.0%
ValueCountFrequency (%)
141.067001 1
2.0%
122.481003 1
2.0%
121.709 1
2.0%
119.689003 1
2.0%
119.356003 1
2.0%
118.049004 1
2.0%
115.538002 1
2.0%
109.725998 1
2.0%
94.489304 1
2.0%
91.346901 1
2.0%

AVE_VE
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.293845
Minimum0
Maximum12.3209
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:21.435126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.25815
Q110.1809
median11.327
Q311.6474
95-th percentile12.18632
Maximum12.3209
Range12.3209
Interquartile range (IQR)1.4665

Descriptive statistics

Standard deviation2.7561062
Coefficient of variation (CV)0.26774313
Kurtosis6.1373983
Mean10.293845
Median Absolute Deviation (MAD)0.4871
Skewness-2.5683433
Sum504.39839
Variance7.5961217
MonotonicityNot monotonic
2023-12-10T23:32:21.567588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
11.6376 1
 
2.0%
1.41491 1
 
2.0%
11.4661 1
 
2.0%
12.3206 1
 
2.0%
12.0626 1
 
2.0%
2.65967 1
 
2.0%
4.15587 1
 
2.0%
8.93786 1
 
2.0%
11.1706 1
 
2.0%
10.6565 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
1.41491 1
2.0%
2.65967 1
2.0%
4.15587 1
2.0%
5.80005 1
2.0%
8.93554 1
2.0%
8.93786 1
2.0%
9.54507 1
2.0%
9.70452 1
2.0%
9.83519 1
2.0%
ValueCountFrequency (%)
12.3209 1
2.0%
12.3206 1
2.0%
12.2434 1
2.0%
12.1007 1
2.0%
12.0626 1
2.0%
12.0486 1
2.0%
11.8924 1
2.0%
11.871 1
2.0%
11.8141 1
2.0%
11.7416 1
2.0%

MAX_VE
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.58455
Minimum0
Maximum17.3406
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:21.702198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.688256
Q113.7788
median14.3286
Q314.8625
95-th percentile16.45416
Maximum17.3406
Range17.3406
Interquartile range (IQR)1.0837

Descriptive statistics

Standard deviation3.1924143
Coefficient of variation (CV)0.23500332
Kurtosis8.1960848
Mean13.58455
Median Absolute Deviation (MAD)0.5498
Skewness-2.7765167
Sum665.64295
Variance10.191509
MonotonicityNot monotonic
2023-12-10T23:32:21.826253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
14.0425 1
 
2.0%
4.8547 1
 
2.0%
14.8037 1
 
2.0%
14.98 1
 
2.0%
16.8039 1
 
2.0%
4.27707 1
 
2.0%
6.93859 1
 
2.0%
12.3088 1
 
2.0%
13.7436 1
 
2.0%
13.8094 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
4.27707 1
2.0%
4.8547 1
2.0%
6.93859 1
2.0%
9.47339 1
2.0%
12.3088 1
2.0%
13.0608 1
2.0%
13.1343 1
2.0%
13.4803 1
2.0%
13.6525 1
2.0%
ValueCountFrequency (%)
17.3406 1
2.0%
16.8039 1
2.0%
16.5948 1
2.0%
16.2432 1
2.0%
15.503 1
2.0%
15.491 1
2.0%
15.486 1
2.0%
15.1821 1
2.0%
15.1634 1
2.0%
14.98 1
2.0%

NVGTN_DIST
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22302.123
Minimum0
Maximum37137
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:21.941366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5021.616
Q116634.7
median24295.8
Q329126.2
95-th percentile35264.62
Maximum37137
Range37137
Interquartile range (IQR)12491.5

Descriptive statistics

Standard deviation9360.1333
Coefficient of variation (CV)0.41969697
Kurtosis-0.2653229
Mean22302.123
Median Absolute Deviation (MAD)6105.8
Skewness-0.62592053
Sum1092804
Variance87612095
MonotonicityNot monotonic
2023-12-10T23:32:22.061100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
30725.7 1
 
2.0%
1140.11 1
 
2.0%
21311.4 1
 
2.0%
34892.8 1
 
2.0%
26702.4 1
 
2.0%
4911.28 1
 
2.0%
5187.12 1
 
2.0%
6842.92 1
 
2.0%
31547.6 1
 
2.0%
18601.0 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
1140.11 1
2.0%
4911.28 1
2.0%
5187.12 1
2.0%
6842.92 1
2.0%
9375.48 1
2.0%
11834.8 1
2.0%
12718.9 1
2.0%
12797.4 1
2.0%
13662.1 1
2.0%
ValueCountFrequency (%)
37137.0 1
2.0%
35699.1 1
2.0%
35512.5 1
2.0%
34892.8 1
2.0%
33427.5 1
2.0%
31986.0 1
2.0%
31861.8 1
2.0%
31547.6 1
2.0%
30868.2 1
2.0%
30725.7 1
2.0%

WAVE_MAX_CYCL
Real number (ℝ)

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.260867
Minimum10.8696
Maximum21.7391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:22.173229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.8696
5-th percentile13.84492
Q118.1818
median20.4082
Q320.4082
95-th percentile21.7391
Maximum21.7391
Range10.8695
Interquartile range (IQR)2.2264

Descriptive statistics

Standard deviation2.5260261
Coefficient of variation (CV)0.1311481
Kurtosis2.6831225
Mean19.260867
Median Absolute Deviation (MAD)1.1774
Skewness-1.6607443
Sum943.7825
Variance6.380808
MonotonicityNot monotonic
2023-12-10T23:32:22.293193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20.4082 16
32.7%
21.7391 9
18.4%
20.0 5
 
10.2%
19.2308 3
 
6.1%
15.625 3
 
6.1%
18.8679 2
 
4.1%
17.8571 2
 
4.1%
17.2414 2
 
4.1%
20.8333 1
 
2.0%
17.5439 1
 
2.0%
Other values (5) 5
 
10.2%
ValueCountFrequency (%)
10.8696 1
 
2.0%
12.3457 1
 
2.0%
12.6582 1
 
2.0%
15.625 3
6.1%
16.6667 1
 
2.0%
17.2414 2
4.1%
17.5439 1
 
2.0%
17.8571 2
4.1%
18.1818 1
 
2.0%
18.8679 2
4.1%
ValueCountFrequency (%)
21.7391 9
18.4%
20.8333 1
 
2.0%
20.4082 16
32.7%
20.0 5
 
10.2%
19.2308 3
 
6.1%
18.8679 2
 
4.1%
18.1818 1
 
2.0%
17.8571 2
 
4.1%
17.5439 1
 
2.0%
17.2414 2
 
4.1%

WAVE_AVE_CYCL
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8548414
Minimum4.49931
Maximum15.625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:22.408860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.49931
5-th percentile5.178754
Q16.07572
median7.98594
Q38.88118
95-th percentile11.15832
Maximum15.625
Range11.12569
Interquartile range (IQR)2.80546

Descriptive statistics

Standard deviation2.1234199
Coefficient of variation (CV)0.27033263
Kurtosis2.4158255
Mean7.8548414
Median Absolute Deviation (MAD)1.48116
Skewness1.0805615
Sum384.88723
Variance4.5089121
MonotonicityNot monotonic
2023-12-10T23:32:22.530257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8.09369 1
 
2.0%
5.64103 1
 
2.0%
8.94611 1
 
2.0%
8.05667 1
 
2.0%
8.5546 1
 
2.0%
4.49931 1
 
2.0%
4.85856 1
 
2.0%
5.85208 1
 
2.0%
6.63999 1
 
2.0%
8.04972 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
4.49931 1
2.0%
4.85856 1
2.0%
5.14077 1
2.0%
5.23573 1
2.0%
5.28024 1
2.0%
5.58788 1
2.0%
5.64103 1
2.0%
5.71288 1
2.0%
5.85208 1
2.0%
5.95342 1
2.0%
ValueCountFrequency (%)
15.625 1
2.0%
11.9564 1
2.0%
11.2492 1
2.0%
11.022 1
2.0%
10.6519 1
2.0%
10.6002 1
2.0%
9.81777 1
2.0%
9.6512 1
2.0%
9.48719 1
2.0%
9.23918 1
2.0%

WAVE_MAX_HGHT
Real number (ℝ)

Distinct38
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7873469
Minimum0.39
Maximum9.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:22.657852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.39
5-th percentile2.554
Q13.89
median4.85
Q35.66
95-th percentile7.352
Maximum9.02
Range8.63
Interquartile range (IQR)1.77

Descriptive statistics

Standard deviation1.545818
Coefficient of variation (CV)0.32289659
Kurtosis1.1446588
Mean4.7873469
Median Absolute Deviation (MAD)0.84
Skewness0.10076541
Sum234.58
Variance2.3895532
MonotonicityNot monotonic
2023-12-10T23:32:22.778245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
5.66 4
 
8.2%
4.85 3
 
6.1%
5.14 2
 
4.1%
4.6 2
 
4.1%
3.15 2
 
4.1%
3.47 2
 
4.1%
2.37 2
 
4.1%
5.52 2
 
4.1%
3.82 1
 
2.0%
5.85 1
 
2.0%
Other values (28) 28
57.1%
ValueCountFrequency (%)
0.39 1
2.0%
2.37 2
4.1%
2.83 1
2.0%
2.85 1
2.0%
3.15 2
4.1%
3.41 1
2.0%
3.47 2
4.1%
3.67 1
2.0%
3.82 1
2.0%
3.89 1
2.0%
ValueCountFrequency (%)
9.02 1
 
2.0%
7.96 1
 
2.0%
7.54 1
 
2.0%
7.07 1
 
2.0%
6.91 1
 
2.0%
6.6 1
 
2.0%
6.32 1
 
2.0%
5.97 1
 
2.0%
5.85 1
 
2.0%
5.66 4
8.2%

WAVE_AVE_HGHT
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1926564
Minimum0.39
Maximum2.37155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:22.917466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.39
5-th percentile0.5545564
Q10.932165
median1.17341
Q31.31749
95-th percentile1.991006
Maximum2.37155
Range1.98155
Interquartile range (IQR)0.385325

Descriptive statistics

Standard deviation0.40837014
Coefficient of variation (CV)0.34240383
Kurtosis1.0740026
Mean1.1926564
Median Absolute Deviation (MAD)0.225034
Skewness0.71531787
Sum58.440166
Variance0.16676617
MonotonicityNot monotonic
2023-12-10T23:32:23.046335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1.13985 1
 
2.0%
1.19933 1
 
2.0%
1.81331 1
 
2.0%
1.1843 1
 
2.0%
1.46045 1
 
2.0%
0.471686 1
 
2.0%
0.465114 1
 
2.0%
0.996527 1
 
2.0%
0.932165 1
 
2.0%
0.822637 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.39 1
2.0%
0.465114 1
2.0%
0.471686 1
2.0%
0.678862 1
2.0%
0.742671 1
2.0%
0.80119 1
2.0%
0.822637 1
2.0%
0.842013 1
2.0%
0.898601 1
2.0%
0.914778 1
2.0%
ValueCountFrequency (%)
2.37155 1
2.0%
2.13696 1
2.0%
2.10947 1
2.0%
1.81331 1
2.0%
1.76113 1
2.0%
1.74971 1
2.0%
1.63692 1
2.0%
1.54055 1
2.0%
1.505 1
2.0%
1.47465 1
2.0%

MAX_WDSP
Real number (ℝ)

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.681517
Minimum4.46611
Maximum47.7164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:23.166856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.46611
5-th percentile26.15548
Q130.2784
median33.9973
Q340.8659
95-th percentile44.12396
Maximum47.7164
Range43.25029
Interquartile range (IQR)10.5875

Descriptive statistics

Standard deviation7.4521698
Coefficient of variation (CV)0.21487439
Kurtosis4.272327
Mean34.681517
Median Absolute Deviation (MAD)4.6625
Skewness-1.2214834
Sum1699.3943
Variance55.534835
MonotonicityNot monotonic
2023-12-10T23:32:23.277791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
29.3348 2
 
4.1%
32.7007 1
 
2.0%
30.8455 1
 
2.0%
37.1337 1
 
2.0%
35.3421 1
 
2.0%
29.9455 1
 
2.0%
29.8756 1
 
2.0%
30.2784 1
 
2.0%
28.9625 1
 
2.0%
32.8901 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
4.46611 1
2.0%
21.9606 1
2.0%
25.8864 1
2.0%
26.5591 1
2.0%
27.523 1
2.0%
27.6056 1
2.0%
28.9625 1
2.0%
29.2743 1
2.0%
29.3348 2
4.1%
29.8756 1
2.0%
ValueCountFrequency (%)
47.7164 1
2.0%
47.225 1
2.0%
44.1516 1
2.0%
44.0825 1
2.0%
42.6927 1
2.0%
42.025 1
2.0%
41.6508 1
2.0%
41.6481 1
2.0%
41.6204 1
2.0%
41.4407 1
2.0%

AVE_WDSP
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.295079
Minimum4.46611
Maximum13.3474
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:23.393028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.46611
5-th percentile7.645604
Q19.48114
median10.1822
Q311.6151
95-th percentile12.64206
Maximum13.3474
Range8.88129
Interquartile range (IQR)2.13396

Descriptive statistics

Standard deviation1.7146045
Coefficient of variation (CV)0.16654602
Kurtosis1.6078959
Mean10.295079
Median Absolute Deviation (MAD)0.91491
Skewness-0.66544118
Sum504.45887
Variance2.9398684
MonotonicityNot monotonic
2023-12-10T23:32:23.499811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10.0807 1
 
2.0%
6.99002 1
 
2.0%
11.5918 1
 
2.0%
9.86685 1
 
2.0%
10.3163 1
 
2.0%
8.19295 1
 
2.0%
8.94559 1
 
2.0%
7.38804 1
 
2.0%
9.88196 1
 
2.0%
9.65064 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
4.46611 1
2.0%
6.99002 1
2.0%
7.38804 1
2.0%
8.03195 1
2.0%
8.19295 1
2.0%
8.63001 1
2.0%
8.80571 1
2.0%
8.94559 1
2.0%
9.00329 1
2.0%
9.22691 1
2.0%
ValueCountFrequency (%)
13.3474 1
2.0%
13.2178 1
2.0%
12.6441 1
2.0%
12.639 1
2.0%
12.5346 1
2.0%
12.4912 1
2.0%
12.4849 1
2.0%
12.2632 1
2.0%
12.2399 1
2.0%
12.0091 1
2.0%

ADDTI_RSTC
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31199.721
Minimum0
Maximum73124
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:23.609707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4186.264
Q118265.9
median25536.6
Q345284.7
95-th percentile61250.2
Maximum73124
Range73124
Interquartile range (IQR)27018.8

Descriptive statistics

Standard deviation18220.748
Coefficient of variation (CV)0.58400356
Kurtosis-0.63611535
Mean31199.721
Median Absolute Deviation (MAD)15182.5
Skewness0.36865862
Sum1528786.3
Variance3.3199567 × 108
MonotonicityNot monotonic
2023-12-10T23:32:23.754263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41622.3 1
 
2.0%
8094.58 1
 
2.0%
60971.8 1
 
2.0%
47662.4 1
 
2.0%
46884.0 1
 
2.0%
1580.72 1
 
2.0%
1536.98 1
 
2.0%
19217.6 1
 
2.0%
25536.6 1
 
2.0%
24469.8 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
1536.98 1
2.0%
1580.72 1
2.0%
8094.58 1
2.0%
9720.07 1
2.0%
10249.4 1
2.0%
10310.8 1
2.0%
14801.2 1
2.0%
16021.1 1
2.0%
16089.1 1
2.0%
ValueCountFrequency (%)
73124.0 1
2.0%
68160.3 1
2.0%
61435.8 1
2.0%
60971.8 1
2.0%
58894.0 1
2.0%
53664.1 1
2.0%
53361.4 1
2.0%
51777.7 1
2.0%
47662.4 1
2.0%
46884.0 1
2.0%

TOT_RSTC
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean636618.74
Minimum0
Maximum1316320
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:23.907418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5609.442
Q1403022
median638272
Q3942262
95-th percentile1126250
Maximum1316320
Range1316320
Interquartile range (IQR)539240

Descriptive statistics

Standard deviation371908.6
Coefficient of variation (CV)0.5841936
Kurtosis-1.010222
Mean636618.74
Median Absolute Deviation (MAD)295214
Skewness-0.12907135
Sum31194318
Variance1.38316 × 1011
MonotonicityNot monotonic
2023-12-10T23:32:24.054980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
890231.0 1
 
2.0%
910.122 1
 
2.0%
668456.0 1
 
2.0%
1110110.0 1
 
2.0%
1263320.0 1
 
2.0%
3465.15 1
 
2.0%
8825.88 1
 
2.0%
115089.0 1
 
2.0%
1017150.0 1
 
2.0%
557015.0 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
910.122 1
2.0%
3465.15 1
2.0%
8825.88 1
2.0%
35216.2 1
2.0%
115089.0 1
2.0%
140822.0 1
2.0%
220788.0 1
2.0%
233155.0 1
2.0%
260778.0 1
2.0%
ValueCountFrequency (%)
1316320.0 1
2.0%
1263320.0 1
2.0%
1137010.0 1
2.0%
1110110.0 1
2.0%
1106160.0 1
2.0%
1101080.0 1
2.0%
1094640.0 1
2.0%
1038450.0 1
2.0%
1027600.0 1
2.0%
1017150.0 1
2.0%

RL_POWER
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7063849.5
Minimum130
Maximum16053100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:24.178178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile93013.94
Q14460130
median6958740
Q310507900
95-th percentile13690420
Maximum16053100
Range16052970
Interquartile range (IQR)6047770

Descriptive statistics

Standard deviation4276384.7
Coefficient of variation (CV)0.60539012
Kurtosis-0.92170059
Mean7063849.5
Median Absolute Deviation (MAD)3323060
Skewness0.036322561
Sum3.4612863 × 108
Variance1.8287466 × 1013
MonotonicityNot monotonic
2023-12-10T23:32:24.295616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
11313400.0 1
 
2.0%
72591.6 1
 
2.0%
7307860.0 1
 
2.0%
13815900.0 1
 
2.0%
16053100.0 1
 
2.0%
80381.9 1
 
2.0%
111962.0 1
 
2.0%
1251720.0 1
 
2.0%
10281800.0 1
 
2.0%
5603090.0 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
130.0 1
2.0%
72591.6 1
2.0%
80381.9 1
2.0%
111962.0 1
2.0%
1127400.0 1
2.0%
1251720.0 1
2.0%
1474030.0 1
2.0%
1920770.0 1
2.0%
2318510.0 1
2.0%
2379110.0 1
2.0%
ValueCountFrequency (%)
16053100.0 1
2.0%
13974600.0 1
2.0%
13815900.0 1
2.0%
13502200.0 1
2.0%
12449400.0 1
2.0%
12411100.0 1
2.0%
11828600.0 1
2.0%
11756900.0 1
2.0%
11597500.0 1
2.0%
11313400.0 1
2.0%

FUEL_CNSMP_QTY
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.493783 × 109
Minimum0
Maximum1.75302 × 1010
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:24.690813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.256964 × 108
Q14.63947 × 109
median7.24002 × 109
Q31.11247 × 1010
95-th percentile1.458274 × 1010
Maximum1.75302 × 1010
Range1.75302 × 1010
Interquartile range (IQR)6.48523 × 109

Descriptive statistics

Standard deviation4.5588048 × 109
Coefficient of variation (CV)0.60834492
Kurtosis-0.8010977
Mean7.493783 × 109
Median Absolute Deviation (MAD)3.39478 × 109
Skewness0.11820533
Sum3.6719537 × 1011
Variance2.0782701 × 1019
MonotonicityNot monotonic
2023-12-10T23:32:24.818207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
12748000000 1
 
2.0%
97661900 1
 
2.0%
7589900000 1
 
2.0%
15102700000 1
 
2.0%
17530200000 1
 
2.0%
110712000 1
 
2.0%
148173000 1
 
2.0%
1387730000 1
 
2.0%
10634800000 1
 
2.0%
5790650000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0 1
2.0%
97661900 1
2.0%
110712000 1
2.0%
148173000 1
2.0%
1387730000 1
2.0%
1614790000 1
2.0%
1813360000 1
2.0%
1922150000 1
2.0%
2312210000 1
2.0%
2625120000 1
2.0%
ValueCountFrequency (%)
17530200000 1
2.0%
15384000000 1
2.0%
15102700000 1
2.0%
13802800000 1
2.0%
13688700000 1
2.0%
13658000000 1
2.0%
12748000000 1
2.0%
12492800000 1
2.0%
11980200000 1
2.0%
11921100000 1
2.0%

CDBX
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3411595 × 1010
Minimum0
Maximum5.4589 × 1010
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:24.946834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.91412 × 108
Q11.48741 × 1010
median2.25453 × 1010
Q33.46423 × 1010
95-th percentile4.541044 × 1010
Maximum5.4589 × 1010
Range5.4589 × 1010
Interquartile range (IQR)1.97682 × 1010

Descriptive statistics

Standard deviation1.4225207 × 1010
Coefficient of variation (CV)0.60761374
Kurtosis-0.81794451
Mean2.3411595 × 1010
Median Absolute Deviation (MAD)1.15256 × 1010
Skewness0.11177395
Sum1.1471682 × 1012
Variance2.0235651 × 1020
MonotonicityNot monotonic
2023-12-10T23:32:25.084978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
39697400000 1
 
2.0%
304116000 1
 
2.0%
23634900000 1
 
2.0%
47029600000 1
 
2.0%
54589000000 1
 
2.0%
344752000 1
 
2.0%
461402000 1
 
2.0%
4321370000 1
 
2.0%
33116800000 1
 
2.0%
18032200000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0 1
2.0%
304116000 1
2.0%
344752000 1
2.0%
461402000 1
2.0%
4321370000 1
2.0%
5177040000 1
2.0%
5646800000 1
2.0%
5985590000 1
2.0%
7200230000 1
2.0%
8416150000 1
2.0%
ValueCountFrequency (%)
54589000000 1
2.0%
47905800000 1
2.0%
47029600000 1
2.0%
42981700000 1
2.0%
42626700000 1
2.0%
42530900000 1
2.0%
39697400000 1
2.0%
38902400000 1
2.0%
38182900000 1
2.0%
37306400000 1
2.0%

NOX
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65476807
Minimum2210
Maximum2.01087 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:25.468495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2210
5-th percentile1683550
Q120966300
median42197900
Q399755700
95-th percentile1.83933 × 108
Maximum2.01087 × 108
Range2.0108479 × 108
Interquartile range (IQR)78789400

Descriptive statistics

Standard deviation59493090
Coefficient of variation (CV)0.90861319
Kurtosis-0.26225107
Mean65476807
Median Absolute Deviation (MAD)27033500
Skewness0.97863524
Sum3.2083635 × 109
Variance3.5394278 × 1015
MonotonicityNot monotonic
2023-12-10T23:32:25.946203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
38465400 1
 
2.0%
246811 1
 
2.0%
24846700 1
 
2.0%
46973900 1
 
2.0%
54580400 1
 
2.0%
1454910 1
 
2.0%
2026510 1
 
2.0%
21279200 1
 
2.0%
174791000 1
 
2.0%
95253400 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2210 1
2.0%
246811 1
2.0%
1454910 1
2.0%
2026510 1
2.0%
3833160 1
2.0%
6530600 1
2.0%
7882930 1
2.0%
15164400 1
2.0%
18577400 1
2.0%
19034100 1
2.0%
ValueCountFrequency (%)
201087000 1
2.0%
194431000 1
2.0%
187465000 1
2.0%
178635000 1
2.0%
174791000 1
2.0%
167004000 1
2.0%
151559000 1
2.0%
151405000 1
2.0%
129336000 1
2.0%
126968000 1
2.0%

SOX
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8715567 × 108
Minimum0
Maximum6.71747 × 108
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:26.384690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4816540
Q11.77782 × 108
median2.77431 × 108
Q34.26288 × 108
95-th percentile5.587974 × 108
Maximum6.71747 × 108
Range6.71747 × 108
Interquartile range (IQR)2.48506 × 108

Descriptive statistics

Standard deviation1.7468989 × 108
Coefficient of variation (CV)0.60834561
Kurtosis-0.80108277
Mean2.8715567 × 108
Median Absolute Deviation (MAD)1.30087 × 108
Skewness0.11821367
Sum1.4070628 × 1010
Variance3.0516558 × 1016
MonotonicityNot monotonic
2023-12-10T23:32:26.590936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
488494000 1
 
2.0%
3742270 1
 
2.0%
290838000 1
 
2.0%
578721000 1
 
2.0%
671747000 1
 
2.0%
4242340 1
 
2.0%
5677840 1
 
2.0%
53176900 1
 
2.0%
407518000 1
 
2.0%
221894000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0 1
2.0%
3742270 1
2.0%
4242340 1
2.0%
5677840 1
2.0%
53176900 1
2.0%
61877500 1
2.0%
69486500 1
2.0%
73655400 1
2.0%
88602100 1
2.0%
100593000 1
2.0%
ValueCountFrequency (%)
671747000 1
2.0%
589506000 1
2.0%
578721000 1
2.0%
528912000 1
2.0%
524540000 1
2.0%
523362000 1
2.0%
488494000 1
2.0%
478712000 1
2.0%
459073000 1
2.0%
456807000 1
2.0%

MTHN
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70638.477
Minimum1.3
Maximum160530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:26.823394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile930.1406
Q144601.3
median69587
Q3105079
95-th percentile136904.2
Maximum160530
Range160528.7
Interquartile range (IQR)60477.7

Descriptive statistics

Standard deviation42763.778
Coefficient of variation (CV)0.6053893
Kurtosis-0.92170913
Mean70638.477
Median Absolute Deviation (MAD)33231
Skewness0.036315433
Sum3461285.4
Variance1.8287407 × 109
MonotonicityNot monotonic
2023-12-10T23:32:27.074218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
113134.0 1
 
2.0%
725.915 1
 
2.0%
73078.6 1
 
2.0%
138159.0 1
 
2.0%
160530.0 1
 
2.0%
803.821 1
 
2.0%
1119.62 1
 
2.0%
12517.1 1
 
2.0%
102818.0 1
 
2.0%
56031.2 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1.3 1
2.0%
725.915 1
2.0%
803.821 1
2.0%
1119.62 1
2.0%
11274.0 1
2.0%
12517.1 1
2.0%
14740.3 1
2.0%
19207.7 1
2.0%
23185.1 1
2.0%
23791.2 1
2.0%
ValueCountFrequency (%)
160530.0 1
2.0%
139746.0 1
2.0%
138159.0 1
2.0%
135022.0 1
2.0%
124494.0 1
2.0%
124111.0 1
2.0%
118286.0 1
2.0%
117568.0 1
2.0%
115975.0 1
2.0%
113134.0 1
2.0%

SHIP_NRG_EFFCN_NVGTN_IDX
Real number (ℝ)

UNIQUE  ZEROS 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.645524
Minimum0
Maximum529.894
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:32:27.289048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.25592
Q117.4971
median24.5816
Q339.0697
95-th percentile140.424
Maximum529.894
Range529.894
Interquartile range (IQR)21.5726

Descriptive statistics

Standard deviation83.21754
Coefficient of variation (CV)1.7465972
Kurtosis24.964849
Mean47.645524
Median Absolute Deviation (MAD)10.3304
Skewness4.7418676
Sum2334.6307
Variance6925.159
MonotonicityNot monotonic
2023-12-10T23:32:27.554320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
39.3925 1
 
2.0%
176.651 1
 
2.0%
17.3318 1
 
2.0%
37.4397 1
 
2.0%
53.7986 1
 
2.0%
44.1484 1
 
2.0%
277.973 1
 
2.0%
21.5312 1
 
2.0%
15.7072 1
 
2.0%
18.5088 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0 1
2.0%
10.6869 1
2.0%
12.9362 1
2.0%
13.7355 1
2.0%
14.1275 1
2.0%
14.1612 1
2.0%
14.2512 1
2.0%
14.5726 1
2.0%
15.1456 1
2.0%
15.7072 1
2.0%
ValueCountFrequency (%)
529.894 1
2.0%
277.973 1
2.0%
176.651 1
2.0%
86.0835 1
2.0%
80.1327 1
2.0%
73.8888 1
2.0%
60.9653 1
2.0%
53.7986 1
2.0%
44.1484 1
2.0%
42.1516 1
2.0%

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
2023-12-10T23:32:27.777017image/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
2023-12-10T23:32:27.979012image/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_LOAVE_VEMAX_VENVGTN_DISTWAVE_MAX_CYCLWAVE_AVE_CYCLWAVE_MAX_HGHTWAVE_AVE_HGHTMAX_WDSPAVE_WDSPADDTI_RSTCTOT_RSTCRL_POWERFUEL_CNSMP_QTYCDBXNOXSOXMTHNSHIP_NRG_EFFCN_NVGTN_IDXRN
02320066519719240STOLT APALChemical/Oil Product29.0185.050.0<NA>25.7615<NA>20163279801-Jan-2022 12:00:0017-Jul-2022 18:00:0036.067101-6.958395.9904979.14610311.637614.042530725.720.83338.093695.141.1398532.700710.080741622.3890231.011313400.0127480000003969740000038465400488494000113134.039.39252
12320066489719264STOLT LINDChemical/Oil Product28.0185.050.0<NA>25.7796<NA>20173282101-Jan-2022 12:00:0017-Jul-2022 18:00:0035.44990217.90449921.472099119.35600311.079613.98331986.019.23086.11983.470.84201331.05899.0032932486.5798972.09753810.011124700000346423000003316300042628800097538.132.99853
22320081019744908STOLT EBONYChemical/Oil Product28.0185.050.0<NA>25.7521<NA>20173278601-Jan-2022 18:00:0017-Jul-2022 12:00:0026.058599124.24400326.16309950.66799911.699414.571831861.820.40827.985946.321.2169342.02510.342651777.7870792.011106200.0124928000003890240000037761000478712000111062.037.24074
32320081029764491STOLT MAPLEChemical/Oil Product28.0185.050.0<NA>25.7827<NA>20173282501-Jan-2022 12:00:0017-Jul-2022 18:00:0027.06290134.47409828.4333.29790111.38614.206235512.520.40828.396175.21.1162137.309310.477745284.71038450.012449400.0136580000004253090000042328100523362000124494.036.48535
42320081049764506STOLT PALMChemical/Oil Product28.0185.050.0<NA>25.7325<NA>20183276101-Jan-2022 12:00:0017-Jul-2022 18:00:0035.1894129.74600242.696301-47.53670111.892414.710835699.120.40828.065583.891.2528130.918410.518853664.1995780.012411100.0136887000004262670000042197900524540000124111.036.44756
53514630009576739BLU TIDEBULK CARRIER27.8171.015.6<NA>28.2106<NA>20113591602-Jan-2022 00:00:0005-Jun-2022 18:00:0031.61350131.487-1.53979-48.780811.496217.340616634.720.40828.221315.661.1525335.190810.0119871.8440124.04785400.04927820000153453000006890980018883000047854.225.68467
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92558064729723655IrinaBulk Carrier32.26225.520.05Trust Bulkers30.0Hudong Zhonghua20168160001-Jan-2022 12:00:0005-Jun-2022 18:00:0030.3727126.208-35.121799-55.363311.226314.008812797.421.73915.712884.341.0412137.791912.644120583.9403022.04460130.04779090000148821000001516440018313100044601.314.251211
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392710027219224673HALIL SAHINBULK CARRIER32.2180.016.55<NA>30.0<NA>20014837703-Jan-2022 12:00:0015-Jul-2022 12:00:001.24802103.8820046.125491.34690111.566614.364419202.021.739111.95647.072.3715540.266213.347468160.3672694.07179350.073329700002283470000012204900028099100071794.124.581641
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462710423899540168BULK FLOWERBULK CARRIER25.0149.513.25<NA>8.29014<NA>20102466816-May-2022 18:00:0016-May-2022 18:00:003.99689.631333.99689.631330.00.00.015.62515.6250.390.394.466114.466110.00.0130.000221001.30.048
472710425669586411Ulusoy 11Bulk Carrier32.26222.020.25Ulusoy Denizyollari30.0Jiangsu Eastern20117942201-Jan-2022 12:00:0017-Jul-2022 18:00:00-10.106348.777136.07210216.02179911.582715.163437137.021.73917.645739.021.1782340.033711.092340719.11316320.013502200.01380280000042981700000194431000528912000135022.014.572649
482710425679586423Ulusoy-12Bulk Carrier32.26222.020.25Ulusoy Denizyollari30.0Jiangsu Eastern20117940301-Jan-2022 12:00:0017-Jul-2022 18:00:0052.4572984.104337.285594.48930411.582214.862530868.221.73918.192617.541.301241.650812.491246354.41106160.011597500.01192110000037122200000167004000456807000115975.015.145650