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
Aggregates Carrier
Other values (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/