Overview

Dataset statistics

Number of variables24
Number of observations49
Missing cells66
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory212.7 B

Variable types

Numeric18
Text3
Categorical3

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001082

Alerts

DPTR_HMS is highly imbalanced (54.2%)Imbalance
SHIP_OWNER_NM has 33 (67.3%) missing valuesMissing
SHPYRD_NM has 33 (67.3%) missing valuesMissing
MMSI has unique valuesUnique
IMO_IDNTF_NO has unique valuesUnique
SHIP_NM has unique valuesUnique
DPTRP_LA has unique valuesUnique
DPTRP_LO has unique valuesUnique
DTNT_LA has unique valuesUnique
DTNT_LO has unique valuesUnique
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
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:46:54.996862
Analysis finished2023-12-10 14:46:55.296222
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%
Mean3.0940099 × 108
Minimum2.473117 × 108
Maximum3.71415 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:55.378124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.473117 × 108
5-th percentile2.8847533 × 108
Q13.1100013 × 108
median3.110002 × 108
Q33.1100034 × 108
95-th percentile3.110004 × 108
Maximum3.71415 × 108
Range1.241033 × 108
Interquartile range (IQR)208

Descriptive statistics

Standard deviation14719626
Coefficient of variation (CV)0.047574592
Kurtosis13.393892
Mean3.0940099 × 108
Median Absolute Deviation (MAD)93
Skewness-0.63725914
Sum1.5160649 × 1010
Variance2.1666739 × 1014
MonotonicityNot monotonic
2023-12-10T23:46:55.507555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
247311700 1
 
2.0%
311000339 1
 
2.0%
311000239 1
 
2.0%
311000259 1
 
2.0%
311000260 1
 
2.0%
311000269 1
 
2.0%
311000296 1
 
2.0%
311000297 1
 
2.0%
311000298 1
 
2.0%
311000304 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
247311700 1
2.0%
273452590 1
2.0%
273458830 1
2.0%
311000076 1
2.0%
311000080 1
2.0%
311000081 1
2.0%
311000085 1
2.0%
311000099 1
2.0%
311000117 1
2.0%
311000119 1
2.0%
ValueCountFrequency (%)
371415000 1
2.0%
311000403 1
2.0%
311000400 1
2.0%
311000398 1
2.0%
311000384 1
2.0%
311000382 1
2.0%
311000380 1
2.0%
311000368 1
2.0%
311000362 1
2.0%
311000361 1
2.0%

IMO_IDNTF_NO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9522914.6
Minimum8033089
Maximum9750309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:55.648409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8033089
5-th percentile9130592.4
Q19461142
median9636436
Q39696101
95-th percentile9728492.2
Maximum9750309
Range1717220
Interquartile range (IQR)234959

Descriptive statistics

Standard deviation326482.46
Coefficient of variation (CV)0.034283879
Kurtosis11.635893
Mean9522914.6
Median Absolute Deviation (MAD)64769
Skewness-3.1821702
Sum4.6662282 × 108
Variance1.0659079 × 1011
MonotonicityNot monotonic
2023-12-10T23:46:55.793731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9581148 1
 
2.0%
9687849 1
 
2.0%
9636436 1
 
2.0%
9700794 1
 
2.0%
9433559 1
 
2.0%
9701205 1
 
2.0%
9489168 1
 
2.0%
9489170 1
 
2.0%
9412294 1
 
2.0%
9750309 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
8033089 1
2.0%
8315554 1
2.0%
9125580 1
2.0%
9138111 1
2.0%
9286619 1
2.0%
9291432 1
2.0%
9296200 1
2.0%
9316036 1
2.0%
9336880 1
2.0%
9350082 1
2.0%
ValueCountFrequency (%)
9750309 1
2.0%
9738753 1
2.0%
9728497 1
2.0%
9728485 1
2.0%
9727405 1
2.0%
9726023 1
2.0%
9725938 1
2.0%
9717632 1
2.0%
9710139 1
2.0%
9701205 1
2.0%

SHIP_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:46:55.992893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length10.22449
Min length5

Characters and Unicode

Total characters501
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st rowBULK LIMPOPO
2nd rowMARLIN
3rd rowSGV FLOT
4th rowTRIADES
5th rowSTOJA
ValueCountFrequency (%)
african 14
 
16.9%
star 4
 
4.8%
btg 3
 
3.6%
cs 3
 
3.6%
unity 2
 
2.4%
spirit 2
 
2.4%
starling 1
 
1.2%
harrier 1
 
1.2%
mariner 1
 
1.2%
griffon 1
 
1.2%
Other values (51) 51
61.4%
2023-12-10T23:46:56.275854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 55
 
11.0%
R 39
 
7.8%
I 35
 
7.0%
34
 
6.8%
N 31
 
6.2%
S 24
 
4.8%
C 22
 
4.4%
T 21
 
4.2%
F 19
 
3.8%
E 17
 
3.4%
Other values (34) 204
40.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 360
71.9%
Lowercase Letter 107
 
21.4%
Space Separator 34
 
6.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 55
15.3%
R 39
10.8%
I 35
9.7%
N 31
 
8.6%
S 24
 
6.7%
C 22
 
6.1%
T 21
 
5.8%
F 19
 
5.3%
E 17
 
4.7%
O 17
 
4.7%
Other values (14) 80
22.2%
Lowercase Letter
ValueCountFrequency (%)
a 15
14.0%
i 15
14.0%
e 14
13.1%
n 11
10.3%
r 11
10.3%
t 10
9.3%
s 6
 
5.6%
l 6
 
5.6%
h 4
 
3.7%
c 3
 
2.8%
Other values (9) 12
11.2%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 467
93.2%
Common 34
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 55
 
11.8%
R 39
 
8.4%
I 35
 
7.5%
N 31
 
6.6%
S 24
 
5.1%
C 22
 
4.7%
T 21
 
4.5%
F 19
 
4.1%
E 17
 
3.6%
O 17
 
3.6%
Other values (33) 187
40.0%
Common
ValueCountFrequency (%)
34
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 501
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 55
 
11.0%
R 39
 
7.8%
I 35
 
7.0%
34
 
6.8%
N 31
 
6.2%
S 24
 
4.8%
C 22
 
4.4%
T 21
 
4.2%
F 19
 
3.8%
E 17
 
3.4%
Other values (34) 204
40.7%

SHIP_KIND
Categorical

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
Bulk Carrier
24 
BULK CARRIER
22 
SELF DISCHARGING BUL
 
1
Chemical/Oil Product
 
1
Ore or Oil Carrier
 
1

Length

Max length20
Median length12
Mean length12.44898
Min length12

Unique

Unique3 ?
Unique (%)6.1%

Sample

1st rowSELF DISCHARGING BUL
2nd rowChemical/Oil Product
3rd rowOre or Oil Carrier
4th rowBULK CARRIER
5th rowBULK CARRIER

Common Values

ValueCountFrequency (%)
Bulk Carrier 24
49.0%
BULK CARRIER 22
44.9%
SELF DISCHARGING BUL 1
 
2.0%
Chemical/Oil Product 1
 
2.0%
Ore or Oil Carrier 1
 
2.0%

Length

2023-12-10T23:46:56.415480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:46:56.507403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
carrier 47
46.5%
bulk 46
45.5%
self 1
 
1.0%
discharging 1
 
1.0%
bul 1
 
1.0%
chemical/oil 1
 
1.0%
product 1
 
1.0%
ore 1
 
1.0%
or 1
 
1.0%
oil 1
 
1.0%

SHIP_WDTH
Real number (ℝ)

Distinct16
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.848163
Minimum13.46
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:56.602959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.46
5-th percentile23.18
Q130
median32.24
Q332.26
95-th percentile45
Maximum45
Range31.54
Interquartile range (IQR)2.26

Descriptive statistics

Standard deviation5.8378327
Coefficient of variation (CV)0.18330202
Kurtosis3.5536263
Mean31.848163
Median Absolute Deviation (MAD)0.05
Skewness0.010443011
Sum1560.56
Variance34.08029
MonotonicityNot monotonic
2023-12-10T23:46:56.762857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
32.26 14
28.6%
30.0 8
16.3%
32.24 6
12.2%
45.0 5
 
10.2%
32.25 2
 
4.1%
30.4 2
 
4.1%
29.8 2
 
4.1%
32.2 2
 
4.1%
32.0 1
 
2.0%
16.83 1
 
2.0%
Other values (6) 6
12.2%
ValueCountFrequency (%)
13.46 1
 
2.0%
16.83 1
 
2.0%
20.5 1
 
2.0%
27.2 1
 
2.0%
28.5 1
 
2.0%
29.8 2
 
4.1%
30.0 8
16.3%
30.4 2
 
4.1%
30.5 1
 
2.0%
32.0 1
 
2.0%
ValueCountFrequency (%)
45.0 5
 
10.2%
32.26 14
28.6%
32.25 2
 
4.1%
32.24 6
12.2%
32.2 2
 
4.1%
32.19 1
 
2.0%
32.0 1
 
2.0%
30.5 1
 
2.0%
30.4 2
 
4.1%
30.0 8
16.3%

SHIP_LNTH
Real number (ℝ)

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.93653
Minimum118.92
Maximum283.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:56.878356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum118.92
5-th percentile148.13
Q1179.96
median185
Q3218.84
95-th percentile283.5
Maximum283.5
Range164.58
Interquartile range (IQR)38.88

Descriptive statistics

Standard deviation37.277734
Coefficient of variation (CV)0.18738506
Kurtosis1.0692475
Mean198.93653
Median Absolute Deviation (MAD)13
Skewness0.7249441
Sum9747.89
Variance1389.6295
MonotonicityNot monotonic
2023-12-10T23:46:56.988359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
283.5 4
 
8.2%
179.96 4
 
8.2%
226.15 3
 
6.1%
183.3 3
 
6.1%
195.0 3
 
6.1%
185.0 2
 
4.1%
199.96 2
 
4.1%
179.9 2
 
4.1%
172.0 2
 
4.1%
176.6 2
 
4.1%
Other values (19) 22
44.9%
ValueCountFrequency (%)
118.92 1
 
2.0%
121.8 1
 
2.0%
139.95 1
 
2.0%
160.4 1
 
2.0%
172.0 2
4.1%
173.0 1
 
2.0%
176.6 2
4.1%
179.9 2
4.1%
179.96 4
8.2%
179.97 1
 
2.0%
ValueCountFrequency (%)
283.5 4
8.2%
281.61 1
 
2.0%
226.15 3
6.1%
225.0 1
 
2.0%
223.0 1
 
2.0%
222.0 1
 
2.0%
221.5 1
 
2.0%
218.84 2
4.1%
217.0 1
 
2.0%
215.97 1
 
2.0%

SHIP_HGHT
Real number (ℝ)

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.941641
Minimum6.67795
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:57.113509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.67795
5-th percentile12.4
Q117.1
median19.3
Q324.8
95-th percentile50
Maximum50
Range43.32205
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation12.987081
Coefficient of variation (CV)0.54244742
Kurtosis0.444176
Mean23.941641
Median Absolute Deviation (MAD)4.17
Skewness1.3808265
Sum1173.1404
Variance168.66428
MonotonicityNot monotonic
2023-12-10T23:46:57.221361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
50.0 9
18.4%
24.8 4
 
8.2%
20.0 3
 
6.1%
18.5 3
 
6.1%
18.6 3
 
6.1%
18.1 3
 
6.1%
18.0 2
 
4.1%
20.05 2
 
4.1%
19.8 2
 
4.1%
15.0 2
 
4.1%
Other values (14) 16
32.7%
ValueCountFrequency (%)
6.67795 1
2.0%
9.17245 1
2.0%
11.6 1
2.0%
13.6 1
2.0%
14.73 2
4.1%
14.75 2
4.1%
15.0 2
4.1%
15.13 1
2.0%
16.6 1
2.0%
17.1 1
2.0%
ValueCountFrequency (%)
50.0 9
18.4%
24.8 4
8.2%
24.7 1
 
2.0%
20.2 1
 
2.0%
20.1 1
 
2.0%
20.05 2
 
4.1%
20.0 3
 
6.1%
19.8 2
 
4.1%
19.5 1
 
2.0%
19.3 1
 
2.0%

SHIP_OWNER_NM
Text

MISSING 

Distinct9
Distinct (%)56.2%
Missing33
Missing (%)67.3%
Memory size524.0 B
2023-12-10T23:46:57.382249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17.5
Mean length15.375
Min length11

Characters and Unicode

Total characters246
Distinct characters36
Distinct categories6 ?
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 (%)37.5%

Sample

1st rowChartworld Shipping
2nd rowAlcyon Shpg.
3rd rowAlcyon Shpg.
4th rowAlcyon Shpg.
5th rowEfshipping Co SA
ValueCountFrequency (%)
alcyon 4
 
11.1%
shpg 4
 
11.1%
chartworld 3
 
8.3%
shipping 3
 
8.3%
bulk 3
 
8.3%
trading 3
 
8.3%
btg 3
 
8.3%
nomikos 1
 
2.8%
csl 1
 
2.8%
kk 1
 
2.8%
Other values (10) 10
27.8%
2023-12-10T23:46:57.675318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
8.1%
i 17
 
6.9%
o 14
 
5.7%
n 14
 
5.7%
l 13
 
5.3%
r 13
 
5.3%
h 12
 
4.9%
p 12
 
4.9%
g 12
 
4.9%
a 11
 
4.5%
Other values (26) 108
43.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 170
69.1%
Uppercase Letter 46
 
18.7%
Space Separator 20
 
8.1%
Other Punctuation 4
 
1.6%
Open Punctuation 3
 
1.2%
Close Punctuation 3
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 17
 
10.0%
o 14
 
8.2%
n 14
 
8.2%
l 13
 
7.6%
r 13
 
7.6%
h 12
 
7.1%
p 12
 
7.1%
g 12
 
7.1%
a 11
 
6.5%
t 7
 
4.1%
Other values (11) 45
26.5%
Uppercase Letter
ValueCountFrequency (%)
S 9
19.6%
T 6
13.0%
B 6
13.0%
A 6
13.0%
C 6
13.0%
G 4
8.7%
K 4
8.7%
E 2
 
4.3%
M 1
 
2.2%
N 1
 
2.2%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 216
87.8%
Common 30
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 17
 
7.9%
o 14
 
6.5%
n 14
 
6.5%
l 13
 
6.0%
r 13
 
6.0%
h 12
 
5.6%
p 12
 
5.6%
g 12
 
5.6%
a 11
 
5.1%
S 9
 
4.2%
Other values (22) 89
41.2%
Common
ValueCountFrequency (%)
20
66.7%
. 4
 
13.3%
( 3
 
10.0%
) 3
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20
 
8.1%
i 17
 
6.9%
o 14
 
5.7%
n 14
 
5.7%
l 13
 
5.3%
r 13
 
5.3%
h 12
 
4.9%
p 12
 
4.9%
g 12
 
4.9%
a 11
 
4.5%
Other values (26) 108
43.9%

DRAFT
Real number (ℝ)

Distinct32
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.706385
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:57.803066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.4
Q117.583
median26.3989
Q330
95-th percentile30
Maximum30
Range28
Interquartile range (IQR)12.417

Descriptive statistics

Standard deviation9.219469
Coefficient of variation (CV)0.4060298
Kurtosis0.22186632
Mean22.706385
Median Absolute Deviation (MAD)3.6011
Skewness-1.2547967
Sum1112.6129
Variance84.998609
MonotonicityNot monotonic
2023-12-10T23:46:57.931048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
30.0 14
28.6%
5.0 3
 
6.1%
24.6711 2
 
4.1%
2.0 2
 
4.1%
26.3989 1
 
2.0%
26.1143 1
 
2.0%
26.9955 1
 
2.0%
26.9963 1
 
2.0%
17.583 1
 
2.0%
29.4548 1
 
2.0%
Other values (22) 22
44.9%
ValueCountFrequency (%)
2.0 2
4.1%
3.0 1
 
2.0%
4.0 1
 
2.0%
5.0 3
6.1%
7.60386 1
 
2.0%
16.5059 1
 
2.0%
17.5808 1
 
2.0%
17.5819 1
 
2.0%
17.5824 1
 
2.0%
17.583 1
 
2.0%
ValueCountFrequency (%)
30.0 14
28.6%
29.9268 1
 
2.0%
29.6173 1
 
2.0%
29.6118 1
 
2.0%
29.6063 1
 
2.0%
29.4548 1
 
2.0%
27.0544 1
 
2.0%
26.9986 1
 
2.0%
26.9963 1
 
2.0%
26.9955 1
 
2.0%

SHPYRD_NM
Text

MISSING 

Distinct10
Distinct (%)62.5%
Missing33
Missing (%)67.3%
Memory size524.0 B
2023-12-10T23:46:58.083650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length14.625
Min length9

Characters and Unicode

Total characters234
Distinct characters31
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 (%)37.5%

Sample

1st rowSasebo HI
2nd rowSungdong SB
3rd rowSungdong SB
4th rowSungdong SB
5th rowTsuneishi Zosen
ValueCountFrequency (%)
sb 5
13.5%
sungdong 4
10.8%
shipyard 4
10.8%
hi 3
 
8.1%
jmu 3
 
8.1%
sasebo 2
 
5.4%
tsuneishi 2
 
5.4%
zosen 2
 
5.4%
tsu 2
 
5.4%
shipbuilding 2
 
5.4%
Other values (8) 8
21.6%
2023-12-10T23:46:58.378152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
9.0%
i 20
 
8.5%
n 20
 
8.5%
S 19
 
8.1%
a 17
 
7.3%
u 13
 
5.6%
s 13
 
5.6%
d 11
 
4.7%
g 11
 
4.7%
o 10
 
4.3%
Other values (21) 79
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 160
68.4%
Uppercase Letter 51
 
21.8%
Space Separator 21
 
9.0%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 20
12.5%
n 20
12.5%
a 17
10.6%
u 13
8.1%
s 13
8.1%
d 11
 
6.9%
g 11
 
6.9%
o 10
 
6.2%
h 9
 
5.6%
y 6
 
3.8%
Other values (8) 30
18.8%
Uppercase Letter
ValueCountFrequency (%)
S 19
37.3%
B 5
 
9.8%
I 4
 
7.8%
H 4
 
7.8%
J 4
 
7.8%
M 4
 
7.8%
U 4
 
7.8%
T 4
 
7.8%
Z 2
 
3.9%
O 1
 
2.0%
Space Separator
ValueCountFrequency (%)
21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 211
90.2%
Common 23
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 20
 
9.5%
n 20
 
9.5%
S 19
 
9.0%
a 17
 
8.1%
u 13
 
6.2%
s 13
 
6.2%
d 11
 
5.2%
g 11
 
5.2%
o 10
 
4.7%
h 9
 
4.3%
Other values (18) 68
32.2%
Common
ValueCountFrequency (%)
21
91.3%
( 1
 
4.3%
) 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
 
9.0%
i 20
 
8.5%
n 20
 
8.5%
S 19
 
8.1%
a 17
 
7.3%
u 13
 
5.6%
s 13
 
5.6%
d 11
 
4.7%
g 11
 
4.7%
o 10
 
4.3%
Other values (21) 79
33.8%

BULD_YR
Real number (ℝ)

Distinct16
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.449
Minimum1970
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:58.492320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1998.6
Q12010
median2013
Q32015
95-th percentile2016
Maximum2019
Range49
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.2791435
Coefficient of variation (CV)0.004118057
Kurtosis12.678269
Mean2010.449
Median Absolute Deviation (MAD)2
Skewness-3.2307629
Sum98512
Variance68.544218
MonotonicityNot monotonic
2023-12-10T23:46:58.605677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2015 13
26.5%
2013 6
12.2%
2014 5
 
10.2%
2010 4
 
8.2%
2011 3
 
6.1%
2005 3
 
6.1%
2012 3
 
6.1%
2016 3
 
6.1%
2009 2
 
4.1%
2004 1
 
2.0%
Other values (6) 6
12.2%
ValueCountFrequency (%)
1970 1
 
2.0%
1985 1
 
2.0%
1997 1
 
2.0%
2001 1
 
2.0%
2003 1
 
2.0%
2004 1
 
2.0%
2005 3
6.1%
2009 2
4.1%
2010 4
8.2%
2011 3
6.1%
ValueCountFrequency (%)
2019 1
 
2.0%
2016 3
 
6.1%
2015 13
26.5%
2014 5
 
10.2%
2013 6
12.2%
2012 3
 
6.1%
2011 3
 
6.1%
2010 4
 
8.2%
2009 2
 
4.1%
2005 3
 
6.1%

DDWGHT
Real number (ℝ)

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65318.98
Minimum3345
Maximum180202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:46:58.724751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3345
5-th percentile18326
Q137500
median57374
Q376812
95-th percentile179419.2
Maximum180202
Range176857
Interquartile range (IQR)39312

Descriptive statistics

Standard deviation43729.654
Coefficient of variation (CV)0.66947853
Kurtosis2.6102348
Mean65318.98
Median Absolute Deviation (MAD)19874
Skewness1.6857062
Sum3200630
Variance1.9122827 × 109
MonotonicityNot monotonic
2023-12-10T23:46:58.856717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
34402 2
 
4.1%
81084 2
 
4.1%
57300 2
 
4.1%
61286 2
 
4.1%
34365 2
 
4.1%
56074 1
 
2.0%
37707 1
 
2.0%
56784 1
 
2.0%
34373 1
 
2.0%
34444 1
 
2.0%
Other values (34) 34
69.4%
ValueCountFrequency (%)
3345 1
2.0%
7008 1
2.0%
11546 1
2.0%
28496 1
2.0%
34365 2
4.1%
34369 1
2.0%
34370 1
2.0%
34373 1
2.0%
34402 2
4.1%
34444 1
2.0%
ValueCountFrequency (%)
180202 1
2.0%
179455 1
2.0%
179428 1
2.0%
179406 1
2.0%
179354 1
2.0%
82331 1
2.0%
82181 1
2.0%
82039 1
2.0%
81168 1
2.0%
81084 2
4.1%

DPTR_HMS
Categorical

IMBALANCE 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2022 12:00:00
37 
01-Jan-2022 06:00:00
 
2
02-Jan-2022 00:00:00
 
1
14-May-2022 12:00:00
 
1
10-Jan-2022 12:00:00
 
1
Other values (7)
7 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique10 ?
Unique (%)20.4%

Sample

1st row01-Jan-2022 12:00:00
2nd row01-Jan-2022 12:00:00
3rd row02-Jan-2022 00:00:00
4th row14-May-2022 12:00:00
5th row01-Jan-2022 12:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2022 12:00:00 37
75.5%
01-Jan-2022 06:00:00 2
 
4.1%
02-Jan-2022 00:00:00 1
 
2.0%
14-May-2022 12:00:00 1
 
2.0%
10-Jan-2022 12:00:00 1
 
2.0%
06-Jan-2022 00:00:00 1
 
2.0%
05-Jan-2022 12:00:00 1
 
2.0%
04-Jan-2022 00:00:00 1
 
2.0%
03-Jan-2022 00:00:00 1
 
2.0%
06-Jan-2022 18:00:00 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2023-12-10T23:46:58.983416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01-jan-2022 40
40.8%
12:00:00 40
40.8%
00:00:00 5
 
5.1%
06:00:00 2
 
2.0%
06-jan-2022 2
 
2.0%
18:00:00 2
 
2.0%
02-jan-2022 1
 
1.0%
14-may-2022 1
 
1.0%
10-jan-2022 1
 
1.0%
05-jan-2022 1
 
1.0%
Other values (3) 3
 
3.1%

ARVL_HMS
Categorical

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
17-Jul-2022 18:00:00
30 
17-Jul-2022 12:00:00
9 
05-Jun-2022 18:00:00
 
3
16-Jul-2022 18:00:00
 
2
16-Mar-2022 06:00:00
 
1
Other values (4)
4 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique5 ?
Unique (%)10.2%

Sample

1st row05-Jun-2022 18:00:00
2nd row17-Jul-2022 18:00:00
3rd row16-Mar-2022 06:00:00
4th row05-Jun-2022 18:00:00
5th row17-Jul-2022 12:00:00

Common Values

ValueCountFrequency (%)
17-Jul-2022 18:00:00 30
61.2%
17-Jul-2022 12:00:00 9
 
18.4%
05-Jun-2022 18:00:00 3
 
6.1%
16-Jul-2022 18:00:00 2
 
4.1%
16-Mar-2022 06:00:00 1
 
2.0%
13-Jul-2022 18:00:00 1
 
2.0%
17-Jul-2022 00:00:00 1
 
2.0%
11-May-2022 00:00:00 1
 
2.0%
14-Jul-2022 00:00:00 1
 
2.0%

Length

2023-12-10T23:46:59.092296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:46:59.211443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
17-jul-2022 40
40.8%
18:00:00 36
36.7%
12:00:00 9
 
9.2%
05-jun-2022 3
 
3.1%
00:00:00 3
 
3.1%
16-jul-2022 2
 
2.0%
16-mar-2022 1
 
1.0%
06:00:00 1
 
1.0%
13-jul-2022 1
 
1.0%
11-may-2022 1
 
1.0%

DPTRP_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.135246
Minimum-39.098598
Maximum57.068298
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)24.5%
Memory size573.0 B
2023-12-10T23:46:59.371049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-39.098598
5-th percentile-33.17836
Q11.29207
median18.253799
Q332.325199
95-th percentile53.22772
Maximum57.068298
Range96.166896
Interquartile range (IQR)31.033129

Descriptive statistics

Standard deviation26.746065
Coefficient of variation (CV)2.0362058
Kurtosis-0.76244909
Mean13.135246
Median Absolute Deviation (MAD)16.806302
Skewness-0.38648264
Sum643.62706
Variance715.35199
MonotonicityNot monotonic
2023-12-10T23:46:59.518903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
49.120399 1
 
2.0%
7.43 1
 
2.0%
5.33242 1
 
2.0%
21.9473 1
 
2.0%
-33.8274 1
 
2.0%
-28.9233 1
 
2.0%
-39.098598 1
 
2.0%
-23.830601 1
 
2.0%
23.522301 1
 
2.0%
32.325199 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-39.098598 1
2.0%
-34.900002 1
2.0%
-33.8274 1
2.0%
-32.2048 1
2.0%
-31.512199 1
2.0%
-28.9233 1
2.0%
-25.720699 1
2.0%
-23.830601 1
2.0%
-20.2917 1
2.0%
-19.6378 1
2.0%
ValueCountFrequency (%)
57.068298 1
2.0%
54.536201 1
2.0%
54.367199 1
2.0%
51.518501 1
2.0%
49.120399 1
2.0%
44.430901 1
2.0%
40.586498 1
2.0%
38.876999 1
2.0%
36.429298 1
2.0%
35.1035 1
2.0%

DPTRP_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.229911
Minimum-165.30499
Maximum151.49499
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)20.4%
Memory size573.0 B
2023-12-10T23:46:59.686844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-165.30499
5-th percentile-85.795101
Q118.5198
median60.062302
Q3118.132
95-th percentile136.8534
Maximum151.49499
Range316.79999
Interquartile range (IQR)99.612204

Descriptive statistics

Standard deviation75.377008
Coefficient of variation (CV)1.4713476
Kurtosis0.12627261
Mean51.229911
Median Absolute Deviation (MAD)55.651695
Skewness-0.85009867
Sum2510.2656
Variance5681.6933
MonotonicityNot monotonic
2023-12-10T23:46:59.836472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
141.985001 1
 
2.0%
108.244003 1
 
2.0%
81.264999 1
 
2.0%
68.250504 1
 
2.0%
27.4443 1
 
2.0%
32.091801 1
 
2.0%
144.820999 1
 
2.0%
151.494995 1
 
2.0%
119.875999 1
 
2.0%
-40.4832 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-165.304993 1
2.0%
-90.780098 1
2.0%
-89.704903 1
2.0%
-79.930397 1
2.0%
-79.711601 1
2.0%
-50.506802 1
2.0%
-48.160702 1
2.0%
-40.4832 1
2.0%
-40.245602 1
2.0%
-2.25429 1
2.0%
ValueCountFrequency (%)
151.494995 1
2.0%
144.820999 1
2.0%
141.985001 1
2.0%
129.156006 1
2.0%
129.076996 1
2.0%
128.669998 1
2.0%
122.803001 1
2.0%
122.679001 1
2.0%
122.537003 1
2.0%
120.350998 1
2.0%

DTNT_LA
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.297255
Minimum-44.586899
Maximum60.019699
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:46:59.968520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-44.586899
5-th percentile-32.701079
Q1-17.1507
median16.7701
Q333.773499
95-th percentile52.63142
Maximum60.019699
Range104.6066
Interquartile range (IQR)50.924199

Descriptive statistics

Standard deviation30.135841
Coefficient of variation (CV)2.6675366
Kurtosis-1.2060764
Mean11.297255
Median Absolute Deviation (MAD)21.112899
Skewness-0.26752153
Sum553.56549
Variance908.16892
MonotonicityNot monotonic
2023-12-10T23:47:00.447104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
49.194698 1
 
2.0%
31.235901 1
 
2.0%
-16.1164 1
 
2.0%
32.115398 1
 
2.0%
0.99352 1
 
2.0%
29.9214 1
 
2.0%
-20.74 1
 
2.0%
37.071899 1
 
2.0%
-32.528099 1
 
2.0%
-1.93978 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-44.586899 1
2.0%
-40.601799 1
2.0%
-32.757198 1
2.0%
-32.616901 1
2.0%
-32.528099 1
2.0%
-31.132401 1
2.0%
-30.0821 1
2.0%
-27.660101 1
2.0%
-27.391701 1
2.0%
-24.8783 1
2.0%
ValueCountFrequency (%)
60.019699 1
2.0%
56.425598 1
2.0%
54.4823 1
2.0%
49.855099 1
2.0%
49.194698 1
2.0%
47.2085 1
2.0%
45.6436 1
2.0%
44.806999 1
2.0%
39.748901 1
2.0%
37.882999 1
2.0%

DTNT_LO
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.733777
Minimum-139.069
Maximum150.509
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)36.7%
Memory size573.0 B
2023-12-10T23:47:00.588974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-139.069
5-th percentile-110.04216
Q1-60.719398
median31.0042
Q396.151703
95-th percentile144.446
Maximum150.509
Range289.578
Interquartile range (IQR)156.8711

Descriptive statistics

Standard deviation84.270182
Coefficient of variation (CV)3.8773832
Kurtosis-1.1292295
Mean21.733777
Median Absolute Deviation (MAD)75.907803
Skewness-0.16561266
Sum1064.9551
Variance7101.4636
MonotonicityNot monotonic
2023-12-10T23:47:00.767025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
142.009995 1
 
2.0%
122.638 1
 
2.0%
43.1119 1
 
2.0%
119.912003 1
 
2.0%
104.440002 1
 
2.0%
-90.0728 1
 
2.0%
-70.200203 1
 
2.0%
-64.195099 1
 
2.0%
-71.679703 1
 
2.0%
-43.967999 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
-139.069 1
2.0%
-122.740997 1
2.0%
-118.219002 1
2.0%
-97.776901 1
2.0%
-95.374397 1
2.0%
-90.0728 1
2.0%
-90.016098 1
2.0%
-71.679703 1
2.0%
-70.200203 1
2.0%
-67.997299 1
2.0%
ValueCountFrequency (%)
150.509003 1
2.0%
149.134003 1
2.0%
146.070007 1
2.0%
142.009995 1
2.0%
138.158997 1
2.0%
127.265999 1
2.0%
122.638 1
2.0%
119.912003 1
2.0%
119.594002 1
2.0%
107.319 1
2.0%

ADDTI_RSTC
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48685.998
Minimum715.058
Maximum114738
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:00.907119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum715.058
5-th percentile8986.716
Q131660.2
median45512.8
Q368812.2
95-th percentile88757.88
Maximum114738
Range114022.94
Interquartile range (IQR)37152

Descriptive statistics

Standard deviation26400.46
Coefficient of variation (CV)0.54225982
Kurtosis-0.33992179
Mean48685.998
Median Absolute Deviation (MAD)15769.8
Skewness0.30310386
Sum2385613.9
Variance6.969843 × 108
MonotonicityNot monotonic
2023-12-10T23:47:01.116482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
17361.6 1
 
2.0%
82748.8 1
 
2.0%
71291.9 1
 
2.0%
36111.7 1
 
2.0%
41712.9 1
 
2.0%
45512.8 1
 
2.0%
42371.3 1
 
2.0%
35734.2 1
 
2.0%
36901.2 1
 
2.0%
31660.2 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
715.058 1
2.0%
1015.16 1
2.0%
7943.86 1
2.0%
10551.0 1
2.0%
13472.4 1
2.0%
16864.7 1
2.0%
17361.6 1
2.0%
21540.3 1
2.0%
22889.5 1
2.0%
28294.1 1
2.0%
ValueCountFrequency (%)
114738.0 1
2.0%
99505.9 1
2.0%
91598.0 1
2.0%
84497.7 1
2.0%
83951.5 1
2.0%
82748.8 1
2.0%
82144.8 1
2.0%
81324.8 1
2.0%
79815.8 1
2.0%
74820.8 1
2.0%

TOT_RSTC
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean927943.9
Minimum7462.33
Maximum1880890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:01.261850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7462.33
5-th percentile74016.28
Q1761336
median950905
Q31170810
95-th percentile1606876
Maximum1880890
Range1873427.7
Interquartile range (IQR)409474

Descriptive statistics

Standard deviation437898.95
Coefficient of variation (CV)0.4719024
Kurtosis0.048572157
Mean927943.9
Median Absolute Deviation (MAD)194546
Skewness-0.28565226
Sum45469251
Variance1.9175549 × 1011
MonotonicityNot monotonic
2023-12-10T23:47:01.418190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
208496.0 1
 
2.0%
1545370.0 1
 
2.0%
987684.0 1
 
2.0%
737017.0 1
 
2.0%
627088.0 1
 
2.0%
1482920.0 1
 
2.0%
543313.0 1
 
2.0%
756359.0 1
 
2.0%
876948.0 1
 
2.0%
1200110.0 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
7462.33 1
2.0%
43306.0 1
2.0%
47205.8 1
2.0%
114232.0 1
2.0%
208496.0 1
2.0%
224518.0 1
2.0%
307397.0 1
2.0%
543313.0 1
2.0%
548281.0 1
2.0%
627088.0 1
2.0%
ValueCountFrequency (%)
1880890.0 1
2.0%
1679910.0 1
2.0%
1647880.0 1
2.0%
1545370.0 1
2.0%
1538540.0 1
2.0%
1482920.0 1
2.0%
1448140.0 1
2.0%
1401470.0 1
2.0%
1302440.0 1
2.0%
1276910.0 1
2.0%

RL_POWER
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9751763.3
Minimum235349
Maximum19049800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:01.581167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum235349
5-th percentile1139786.8
Q17991970
median9940140
Q312244600
95-th percentile16704160
Maximum19049800
Range18814451
Interquartile range (IQR)4252630

Descriptive statistics

Standard deviation4536247.1
Coefficient of variation (CV)0.46517198
Kurtosis-0.097525562
Mean9751763.3
Median Absolute Deviation (MAD)2126810
Skewness-0.33197402
Sum4.778364 × 108
Variance2.0577538 × 1013
MonotonicityNot monotonic
2023-12-10T23:47:01.726006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2454540 1
 
2.0%
16016600 1
 
2.0%
10876900 1
 
2.0%
8215650 1
 
2.0%
6190120 1
 
2.0%
17091300 1
 
2.0%
5547350 1
 
2.0%
7811110 1
 
2.0%
9277240 1
 
2.0%
13043700 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
235349 1
2.0%
446176 1
2.0%
521718 1
2.0%
2066890 1
2.0%
2161370 1
2.0%
2454540 1
2.0%
3115230 1
2.0%
5289000 1
2.0%
5547350 1
2.0%
6190120 1
2.0%
ValueCountFrequency (%)
19049800 1
2.0%
17091300 1
2.0%
16903400 1
2.0%
16405300 1
2.0%
16016600 1
2.0%
15941500 1
2.0%
15127400 1
2.0%
14994900 1
2.0%
13300900 1
2.0%
13072300 1
2.0%

FUEL_CNSMP_QTY
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9668409 × 109
Minimum3.82856 × 108
Maximum1.92517 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:01.878618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.82856 × 108
5-th percentile1.3233432 × 109
Q18.36328 × 109
median1.02912 × 1010
Q31.25381 × 1010
95-th percentile1.694272 × 1010
Maximum1.92517 × 1010
Range1.8868844 × 1010
Interquartile range (IQR)4.17482 × 109

Descriptive statistics

Standard deviation4.5339522 × 109
Coefficient of variation (CV)0.45490364
Kurtosis-0.08669762
Mean9.9668409 × 109
Median Absolute Deviation (MAD)2.2469 × 109
Skewness-0.32447861
Sum4.883752 × 1011
Variance2.0556723 × 1019
MonotonicityNot monotonic
2023-12-10T23:47:02.021695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2693520000 1
 
2.0%
16284100000 1
 
2.0%
10990500000 1
 
2.0%
8363280000 1
 
2.0%
6378560000 1
 
2.0%
17304800000 1
 
2.0%
5636330000 1
 
2.0%
7868030000 1
 
2.0%
9404240000 1
 
2.0%
13334000000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
382856000 1
2.0%
459325000 1
2.0%
658292000 1
2.0%
2320920000 1
2.0%
2693520000 1
2.0%
3097950000 1
2.0%
3337510000 1
2.0%
5495010000 1
2.0%
5636330000 1
2.0%
6378560000 1
2.0%
ValueCountFrequency (%)
19251700000 1
2.0%
17304800000 1
2.0%
17133600000 1
2.0%
16656400000 1
2.0%
16284100000 1
2.0%
16197300000 1
2.0%
15269100000 1
2.0%
15163100000 1
2.0%
13616700000 1
2.0%
13387300000 1
2.0%

CDBX
Real number (ℝ)

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1036683 × 1010
Minimum1.19221 × 109
Maximum5.99496 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:02.156740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.19221 × 109
5-th percentile4.120888 × 109
Q12.60434 × 1010
median3.20467 × 1010
Q33.90433 × 1010
95-th percentile5.275944 × 1010
Maximum5.99496 × 1010
Range5.875739 × 1010
Interquartile range (IQR)1.29999 × 1010

Descriptive statistics

Standard deviation1.4118672 × 1010
Coefficient of variation (CV)0.45490273
Kurtosis-0.086684454
Mean3.1036683 × 1010
Median Absolute Deviation (MAD)6.9966 × 109
Skewness-0.32448773
Sum1.5207975 × 1012
Variance1.993369 × 1020
MonotonicityNot monotonic
2023-12-10T23:47:02.302968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8387550000 1
 
2.0%
50708400000 1
 
2.0%
34224700000 1
 
2.0%
26043400000 1
 
2.0%
19862900000 1
 
2.0%
53886900000 1
 
2.0%
17551600000 1
 
2.0%
24501000000 1
 
2.0%
29285100000 1
 
2.0%
41521600000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1192210000 1
2.0%
1430340000 1
2.0%
2049940000 1
2.0%
7227310000 1
2.0%
8387550000 1
2.0%
9647030000 1
2.0%
10392800000 1
2.0%
17111400000 1
2.0%
17551600000 1
2.0%
19862900000 1
2.0%
ValueCountFrequency (%)
59949600000 1
2.0%
53886900000 1
2.0%
53353800000 1
2.0%
51867900000 1
2.0%
50708400000 1
2.0%
50438200000 1
2.0%
47547800000 1
2.0%
47218400000 1
2.0%
42402300000 1
2.0%
41688100000 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
2023-12-10T23:47:02.463426image/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
2023-12-10T23:47:02.619416image/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_LOADDTI_RSTCTOT_RSTCRL_POWERFUEL_CNSMP_QTYCDBXRN
02473117009581148BULK LIMPOPOSELF DISCHARGING BUL32.26185.018.0<NA>30.0<NA>20115377601-Jan-2022 12:00:0005-Jun-2022 18:00:0049.120399141.98500149.194698142.00999517361.6208496.02454540269352000083875500002
12734525909585912MARLINChemical/Oil Product16.83139.959.17245<NA>21.1768<NA>2019700801-Jan-2022 12:00:0017-Jul-2022 18:00:0044.43090135.19279944.80699936.47347943.86114232.02161370309795000096470300003
22734588308033089SGV FLOTOre or Oil Carrier13.46118.926.67795<NA>16.5059<NA>1970334502-Jan-2022 00:00:0016-Mar-2022 06:00:0040.58649827.266147.208539.773701715.0587462.3323534938285600011922100004
33714150009350082TRIADESBULK CARRIER27.2160.413.6<NA>22.3825<NA>20052849614-May-2022 12:00:0005-Jun-2022 18:00:0035.1035129.07699656.425598138.1589971015.1647205.844617645932500014303400005
43110000769585558STOJABULK CARRIER32.24182.017.1<NA>30.0<NA>20125200001-Jan-2022 12:00:0017-Jul-2022 12:00:0036.429298-2.2542927.6749-95.37439729743.0868097.089148609058700000282092000006
53110000809601168MYSTRASBULK CARRIER32.26183.318.5<NA>24.6711<NA>20125730001-Jan-2022 12:00:0013-Jul-2022 18:00:00-25.720699-48.160702-30.082131.004243454.6762617.078133307965550000248050000007
63110000819601170ELIKONBULK CARRIER32.26183.318.5<NA>24.6711<NA>20135730001-Jan-2022 12:00:0017-Jul-2022 18:00:0012.129945.563939.74890118.89469921540.31039900.01033190010456600000325622000008
73110000859650937AFRICAN KITEBULK CARRIER32.24195.018.6<NA>26.442<NA>20136141301-Jan-2022 06:00:0016-Jul-2022 18:00:0022.61599968.505611.747-90.01609833270.1761336.079919708372370000260715000009
83110000999316036Star PlanetBulk Carrier32.2218.8419.8Chartworld Shipping30.0Sasebo HI20057681210-Jan-2022 12:00:0017-Jul-2022 18:00:001.3708108.009003-44.586899-4.1594591598.01185570.012244600125539000003909290000010
93110001179616541OLYMPUSBULK CARRIER32.0183.318.5<NA>24.7029<NA>20135737406-Jan-2022 00:00:0017-Jul-2022 18:00:0024.948452.051701-32.757198-60.71939884497.7548281.0528900054950100001711140000011
MMSIIMO_IDNTF_NOSHIP_NMSHIP_KINDSHIP_WDTHSHIP_LNTHSHIP_HGHTSHIP_OWNER_NMDRAFTSHPYRD_NMBULD_YRDDWGHTDPTR_HMSARVL_HMSDPTRP_LADPTRP_LODTNT_LADTNT_LOADDTI_RSTCTOT_RSTCRL_POWERFUEL_CNSMP_QTYCDBXRN
393110003589687851BTG DenaliBulk Carrier32.26226.1520.0Bulk Trading (BTG)30.0JMU Tsu Shipyard20158108401-Jan-2022 12:00:0017-Jul-2022 18:00:0038.876999118.132004-27.66010145.51509968812.21276910.013072300133873000004168810000041
403110003619728485AFRICAN GOSHAWKBulk Carrier30.0179.950.0<NA>26.9963<NA>20163437001-Jan-2022 12:00:0017-Jul-2022 12:00:00-34.90000221.136913.106280.717460373.31170810.012554700125381000003904330000042
413110003629728497AFRICAN WEAVERBulk Carrier30.0179.950.0<NA>26.9955<NA>20163436901-Jan-2022 12:00:0017-Jul-2022 18:00:0029.497601-89.70490349.855099-139.06948890.91049230.011541100115421000003594190000043
423110003689138111CSL SpiritBulk Carrier32.19215.9719.5CSL Americas30.0Jiangnan Shipyard20017001801-Jan-2022 12:00:0017-Jul-2022 12:00:0013.8767-90.78009833.773499-118.21900213472.4937175.09844130101946000003174600000044
433110003809461142Star TraderBulk Carrier32.26222.020.05Chartworld Shipping30.0Tsuneishi Zosen20108218101-Jan-2022 12:00:0017-Jul-2022 12:00:00-20.2917-40.24560232.185501119.59400281324.81302440.013300900136167000004240230000045
443110003829725938UNITY SPIRITBulk Carrier32.26199.9650.0<NA>26.1143<NA>20156065201-Jan-2022 12:00:0017-Jul-2022 18:00:004.73186119.65000254.482313.587849784.6773760.0851232088818700002765800000046
453110003849738753AFRICAN BAZABulk Carrier32.24199.9850.0<NA>26.3989<NA>20156131301-Jan-2022 12:00:0017-Jul-2022 18:00:0031.0122.679001-27.391701-48.26779982144.81448140.015941500161973000005043820000047
463110003989589683Star BettyBulk Carrier32.25223.020.1Chartworld Shipping30.0Hyundai HI (Ulsan)20118116801-Jan-2022 12:00:0017-Jul-2022 18:00:00-19.637860.06230237.88299916.763155571.4865669.0879836091742300002856850000048
473110004009726023UNITY FORCEBulk Carrier32.26199.9650.0<NA>26.1044<NA>20166062901-Jan-2022 12:00:0017-Jul-2022 18:00:0032.203098122.8030010.3168889.3978939065.01045630.011974300122798000003823890000049
483110004039710139BTG RainierBulk Carrier32.26226.1520.0Bulk Trading (BTG)30.0JMU Maizuru Shipyard20158107001-Jan-2022 12:00:0017-Jul-2022 18:00:0054.367199-165.304993-4.16769107.31979815.81125340.011868100121947000003797400000050