Overview

Dataset statistics

Number of variables36
Number of observations82
Missing cells1134
Missing cells (%)38.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.5 KiB
Average record size in memory293.6 B

Variable types

Categorical12
Numeric3
Text21

Dataset

Description광양항을 이용하는 컨테이너선의 기항 스케쥴입니다. 컨테이너터미널명, 선사명, 서비스명, 기항지 및 운항선박에 대한 데이터를 포함합니다. 데이터 갱신주기는 년간입니다.
Author여수광양항만공사
URLhttps://www.data.go.kr/data/15020878/fileData.do

Alerts

기항지17 has constant value ""Constant
기항지1 is highly imbalanced (90.5%)Imbalance
얼라이언스명 has 78 (95.1%) missing valuesMissing
공동운항등선사코드2 has 65 (79.3%) missing valuesMissing
공동운항등선사코드3 has 72 (87.8%) missing valuesMissing
공동운항등선사코드4 has 78 (95.1%) missing valuesMissing
공동운항등선사코드5 has 79 (96.3%) missing valuesMissing
기항지5 has 23 (28.0%) missing valuesMissing
기항지6 has 29 (35.4%) missing valuesMissing
기항지7 has 37 (45.1%) missing valuesMissing
기항지8 has 42 (51.2%) missing valuesMissing
기항지9 has 52 (63.4%) missing valuesMissing
기항지10 has 61 (74.4%) missing valuesMissing
기항지11 has 64 (78.0%) missing valuesMissing
기항지12 has 67 (81.7%) missing valuesMissing
기항지13 has 71 (86.6%) missing valuesMissing
기항지14 has 77 (93.9%) missing valuesMissing
기항지15 has 79 (96.3%) missing valuesMissing
기항지16 has 79 (96.3%) missing valuesMissing
기항지17 has 81 (98.8%) missing valuesMissing
서비스코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:08:43.069183
Analysis finished2023-12-12 06:08:44.168747
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

터미널
Categorical

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
GWCT
45 
KIT
37 

Length

Max length4
Median length4
Mean length3.5487805
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGWCT
2nd rowGWCT
3rd rowGWCT
4th rowGWCT
5th rowGWCT

Common Values

ValueCountFrequency (%)
GWCT 45
54.9%
KIT 37
45.1%

Length

2023-12-12T15:08:44.223365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:08:44.306484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gwct 45
54.9%
kit 37
45.1%

터미널별항차수
Real number (ℝ)

Distinct45
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.195122
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T15:08:44.401645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q331
95-th percentile40.95
Maximum45
Range44
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.238816
Coefficient of variation (CV)0.57743551
Kurtosis-1.0765077
Mean21.195122
Median Absolute Deviation (MAD)10
Skewness0.091069169
Sum1738
Variance149.78862
MonotonicityNot monotonic
2023-12-12T15:08:44.502841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 2
 
2.4%
29 2
 
2.4%
22 2
 
2.4%
23 2
 
2.4%
2 2
 
2.4%
25 2
 
2.4%
26 2
 
2.4%
27 2
 
2.4%
28 2
 
2.4%
30 2
 
2.4%
Other values (35) 62
75.6%
ValueCountFrequency (%)
1 2
2.4%
2 2
2.4%
3 2
2.4%
4 2
2.4%
5 2
2.4%
6 2
2.4%
7 2
2.4%
8 2
2.4%
9 2
2.4%
10 2
2.4%
ValueCountFrequency (%)
45 1
1.2%
44 1
1.2%
43 1
1.2%
42 1
1.2%
41 1
1.2%
40 1
1.2%
39 1
1.2%
38 1
1.2%
37 2
2.4%
36 2
2.4%

선사(코드)
Categorical

Distinct25
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Memory size788.0 B
장금상선(SKR)
12 
고려해운(KMD)
11 
남성해운(NSL)
8 
팬오션(POL)
7 
HMM
6 
Other values (20)
38 

Length

Max length18
Median length9
Mean length7.9390244
Min length3

Unique

Unique10 ?
Unique (%)12.2%

Sample

1st row머스크라인(MAE)
2nd row머스크라인(MAE)
3rd rowMCC
4th rowMSC
5th rowHMM

Common Values

ValueCountFrequency (%)
장금상선(SKR) 12
14.6%
고려해운(KMD) 11
13.4%
남성해운(NSL) 8
 
9.8%
팬오션(POL) 7
 
8.5%
HMM 6
 
7.3%
EAS 5
 
6.1%
머스크라인(MAE) 4
 
4.9%
완하이라인(WHL) 3
 
3.7%
SM상선(SML) 3
 
3.7%
흥아라인(HAS) 3
 
3.7%
Other values (15) 20
24.4%

Length

2023-12-12T15:08:44.612194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
장금상선(skr 12
14.5%
고려해운(kmd 11
13.3%
남성해운(nsl 8
 
9.6%
팬오션(pol 7
 
8.4%
hmm 6
 
7.2%
eas 5
 
6.0%
머스크라인(mae 4
 
4.8%
완하이라인(whl 3
 
3.6%
sm상선(sml 3
 
3.6%
흥아라인(has 3
 
3.6%
Other values (16) 21
25.3%

국적_외국적 구분
Categorical

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
국적
54 
외국적
28 

Length

Max length3
Median length2
Mean length2.3414634
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row외국적
2nd row외국적
3rd row외국적
4th row외국적
5th row국적

Common Values

ValueCountFrequency (%)
국적 54
65.9%
외국적 28
34.1%

Length

2023-12-12T15:08:44.714008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:08:44.802868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국적 54
65.9%
외국적 28
34.1%

얼라이언스명
Text

MISSING 

Distinct3
Distinct (%)75.0%
Missing78
Missing (%)95.1%
Memory size788.0 B
2023-12-12T15:08:44.884917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3.5
Mean length3.5
Min length2

Characters and Unicode

Total characters14
Distinct characters12
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

Unique2 ?
Unique (%)50.0%

Sample

1st row2M
2nd rowTHE A
3rd row2M
4th rowOcean
ValueCountFrequency (%)
2m 2
40.0%
the 1
20.0%
a 1
20.0%
ocean 1
20.0%
2023-12-12T15:08:45.082312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2
14.3%
M 2
14.3%
T 1
7.1%
H 1
7.1%
E 1
7.1%
1
7.1%
A 1
7.1%
O 1
7.1%
c 1
7.1%
e 1
7.1%
Other values (2) 2
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7
50.0%
Lowercase Letter 4
28.6%
Decimal Number 2
 
14.3%
Space Separator 1
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 2
28.6%
T 1
14.3%
H 1
14.3%
E 1
14.3%
A 1
14.3%
O 1
14.3%
Lowercase Letter
ValueCountFrequency (%)
c 1
25.0%
e 1
25.0%
a 1
25.0%
n 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11
78.6%
Common 3
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 2
18.2%
T 1
9.1%
H 1
9.1%
E 1
9.1%
A 1
9.1%
O 1
9.1%
c 1
9.1%
e 1
9.1%
a 1
9.1%
n 1
9.1%
Common
ValueCountFrequency (%)
2 2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2
14.3%
M 2
14.3%
T 1
7.1%
H 1
7.1%
E 1
7.1%
1
7.1%
A 1
7.1%
O 1
7.1%
c 1
7.1%
e 1
7.1%
Other values (2) 2
14.3%

기항선사코드
Categorical

Distinct25
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Memory size788.0 B
SKR
12 
KMD
11 
NSL
8 
POL
7 
HMM
6 
Other values (20)
38 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique10 ?
Unique (%)12.2%

Sample

1st rowMAE
2nd rowMAE
3rd rowMCC
4th rowMSC
5th rowHMM

Common Values

ValueCountFrequency (%)
SKR 12
14.6%
KMD 11
13.4%
NSL 8
 
9.8%
POL 7
 
8.5%
HMM 6
 
7.3%
EAS 5
 
6.1%
MAE 4
 
4.9%
WHL 3
 
3.7%
SML 3
 
3.7%
HAS 3
 
3.7%
Other values (15) 20
24.4%

Length

2023-12-12T15:08:45.185685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
skr 12
14.6%
kmd 11
13.4%
nsl 8
 
9.8%
pol 7
 
8.5%
hmm 6
 
7.3%
eas 5
 
6.1%
mae 4
 
4.9%
whl 3
 
3.7%
sml 3
 
3.7%
has 3
 
3.7%
Other values (15) 20
24.4%

공동운항등선사코드1
Categorical

Distinct18
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
<NA>
41 
KMD
9 
NSL
5 
SKR
 
4
TSL
 
3
Other values (13)
20 

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Unique

Unique8 ?
Unique (%)9.8%

Sample

1st rowMSC
2nd rowCMA
3rd row<NA>
4th row<NA>
5th rowHLC

Common Values

ValueCountFrequency (%)
<NA> 41
50.0%
KMD 9
 
11.0%
NSL 5
 
6.1%
SKR 4
 
4.9%
TSL 3
 
3.7%
HAS 3
 
3.7%
ONE 3
 
3.7%
CKL 2
 
2.4%
DYS 2
 
2.4%
CNC 2
 
2.4%
Other values (8) 8
 
9.8%

Length

2023-12-12T15:08:45.281626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 41
50.0%
kmd 9
 
11.0%
nsl 5
 
6.1%
skr 4
 
4.9%
tsl 3
 
3.7%
has 3
 
3.7%
one 3
 
3.7%
dys 2
 
2.4%
cnc 2
 
2.4%
ckl 2
 
2.4%
Other values (8) 8
 
9.8%

공동운항등선사코드2
Text

MISSING 

Distinct14
Distinct (%)82.4%
Missing65
Missing (%)79.3%
Memory size788.0 B
2023-12-12T15:08:45.399935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters51
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)70.6%

Sample

1st rowAPL
2nd rowONE
3rd rowSIT
4th rowNSL
5th rowGFS
ValueCountFrequency (%)
ckl 3
17.6%
skr 2
11.8%
apl 1
 
5.9%
one 1
 
5.9%
sit 1
 
5.9%
nsl 1
 
5.9%
gfs 1
 
5.9%
pol 1
 
5.9%
djs 1
 
5.9%
pil 1
 
5.9%
Other values (4) 4
23.5%
2023-12-12T15:08:45.715044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 9
17.6%
S 7
13.7%
C 6
11.8%
K 5
9.8%
P 4
7.8%
O 3
 
5.9%
N 2
 
3.9%
I 2
 
3.9%
E 2
 
3.9%
R 2
 
3.9%
Other values (8) 9
17.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 51
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 9
17.6%
S 7
13.7%
C 6
11.8%
K 5
9.8%
P 4
7.8%
O 3
 
5.9%
N 2
 
3.9%
I 2
 
3.9%
E 2
 
3.9%
R 2
 
3.9%
Other values (8) 9
17.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 51
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 9
17.6%
S 7
13.7%
C 6
11.8%
K 5
9.8%
P 4
7.8%
O 3
 
5.9%
N 2
 
3.9%
I 2
 
3.9%
E 2
 
3.9%
R 2
 
3.9%
Other values (8) 9
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 9
17.6%
S 7
13.7%
C 6
11.8%
K 5
9.8%
P 4
7.8%
O 3
 
5.9%
N 2
 
3.9%
I 2
 
3.9%
E 2
 
3.9%
R 2
 
3.9%
Other values (8) 9
17.6%

공동운항등선사코드3
Text

MISSING 

Distinct9
Distinct (%)90.0%
Missing72
Missing (%)87.8%
Memory size788.0 B
2023-12-12T15:08:45.847316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1
Min length3

Characters and Unicode

Total characters31
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)80.0%

Sample

1st rowOOCL
2nd rowSLS
3rd rowDYS
4th rowTSL
5th rowHAS
ValueCountFrequency (%)
has 2
20.0%
oocl 1
10.0%
sls 1
10.0%
dys 1
10.0%
tsl 1
10.0%
esl 1
10.0%
hsl 1
10.0%
sml 1
10.0%
pcl 1
10.0%
2023-12-12T15:08:46.095040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 9
29.0%
L 7
22.6%
H 3
 
9.7%
A 2
 
6.5%
O 2
 
6.5%
C 2
 
6.5%
D 1
 
3.2%
Y 1
 
3.2%
T 1
 
3.2%
E 1
 
3.2%
Other values (2) 2
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 31
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 9
29.0%
L 7
22.6%
H 3
 
9.7%
A 2
 
6.5%
O 2
 
6.5%
C 2
 
6.5%
D 1
 
3.2%
Y 1
 
3.2%
T 1
 
3.2%
E 1
 
3.2%
Other values (2) 2
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 31
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 9
29.0%
L 7
22.6%
H 3
 
9.7%
A 2
 
6.5%
O 2
 
6.5%
C 2
 
6.5%
D 1
 
3.2%
Y 1
 
3.2%
T 1
 
3.2%
E 1
 
3.2%
Other values (2) 2
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 9
29.0%
L 7
22.6%
H 3
 
9.7%
A 2
 
6.5%
O 2
 
6.5%
C 2
 
6.5%
D 1
 
3.2%
Y 1
 
3.2%
T 1
 
3.2%
E 1
 
3.2%
Other values (2) 2
 
6.5%

공동운항등선사코드4
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing78
Missing (%)95.1%
Memory size788.0 B
2023-12-12T15:08:46.222392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowZIM
2nd rowNSL
3rd rowHMM
4th rowONE
ValueCountFrequency (%)
zim 1
25.0%
nsl 1
25.0%
hmm 1
25.0%
one 1
25.0%
2023-12-12T15:08:46.450478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 3
25.0%
N 2
16.7%
Z 1
 
8.3%
I 1
 
8.3%
S 1
 
8.3%
L 1
 
8.3%
H 1
 
8.3%
O 1
 
8.3%
E 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 3
25.0%
N 2
16.7%
Z 1
 
8.3%
I 1
 
8.3%
S 1
 
8.3%
L 1
 
8.3%
H 1
 
8.3%
O 1
 
8.3%
E 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 12
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 3
25.0%
N 2
16.7%
Z 1
 
8.3%
I 1
 
8.3%
S 1
 
8.3%
L 1
 
8.3%
H 1
 
8.3%
O 1
 
8.3%
E 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 3
25.0%
N 2
16.7%
Z 1
 
8.3%
I 1
 
8.3%
S 1
 
8.3%
L 1
 
8.3%
H 1
 
8.3%
O 1
 
8.3%
E 1
 
8.3%

공동운항등선사코드5
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing79
Missing (%)96.3%
Memory size788.0 B
2023-12-12T15:08:46.606999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowHAS
2nd rowDJS
3rd rowEMC
ValueCountFrequency (%)
has 1
33.3%
djs 1
33.3%
emc 1
33.3%
2023-12-12T15:08:46.836349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 2
22.2%
H 1
11.1%
A 1
11.1%
D 1
11.1%
J 1
11.1%
E 1
11.1%
M 1
11.1%
C 1
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 2
22.2%
H 1
11.1%
A 1
11.1%
D 1
11.1%
J 1
11.1%
E 1
11.1%
M 1
11.1%
C 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 9
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 2
22.2%
H 1
11.1%
A 1
11.1%
D 1
11.1%
J 1
11.1%
E 1
11.1%
M 1
11.1%
C 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 2
22.2%
H 1
11.1%
A 1
11.1%
D 1
11.1%
J 1
11.1%
E 1
11.1%
M 1
11.1%
C 1
11.1%

서비스지역
Categorical

Distinct8
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size788.0 B
동북아
38 
동남아
30 
북미
5 
중동
 
3
유럽
 
2
Other values (3)
4 

Length

Max length4
Median length3
Mean length2.8902439
Min length2

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st row유럽
2nd row중동
3rd row동남아
4th row아프리카
5th row북미

Common Values

ValueCountFrequency (%)
동북아 38
46.3%
동남아 30
36.6%
북미 5
 
6.1%
중동 3
 
3.7%
유럽 2
 
2.4%
아프리카 2
 
2.4%
중남미 1
 
1.2%
연안 1
 
1.2%

Length

2023-12-12T15:08:46.953839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:08:47.103754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북아 38
46.3%
동남아 30
36.6%
북미 5
 
6.1%
중동 3
 
3.7%
유럽 2
 
2.4%
아프리카 2
 
2.4%
중남미 1
 
1.2%
연안 1
 
1.2%

서비스코드
Text

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-12T15:08:47.407239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length3.6829268
Min length3

Characters and Unicode

Total characters302
Distinct characters32
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

Unique82 ?
Unique (%)100.0%

Sample

1st rowNEU2
2nd rowFI3
3rd rowIA5
4th rowAFRICA
5th rowPN4
ValueCountFrequency (%)
neu2 1
 
1.2%
kck 1
 
1.2%
jtk3 1
 
1.2%
khs1 1
 
1.2%
khp 1
 
1.2%
ksh 1
 
1.2%
nkt 1
 
1.2%
ais 1
 
1.2%
vts 1
 
1.2%
cki2 1
 
1.2%
Other values (72) 72
87.8%
2023-12-12T15:08:47.919018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 42
13.9%
S 39
12.9%
N 28
 
9.3%
C 26
 
8.6%
X 16
 
5.3%
T 13
 
4.3%
P 13
 
4.3%
J 11
 
3.6%
1 11
 
3.6%
B 10
 
3.3%
Other values (22) 93
30.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 259
85.8%
Decimal Number 31
 
10.3%
Open Punctuation 6
 
2.0%
Close Punctuation 5
 
1.7%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 42
16.2%
S 39
15.1%
N 28
10.8%
C 26
10.0%
X 16
 
6.2%
T 13
 
5.0%
P 13
 
5.0%
J 11
 
4.2%
B 10
 
3.9%
H 10
 
3.9%
Other values (13) 51
19.7%
Decimal Number
ValueCountFrequency (%)
1 11
35.5%
2 10
32.3%
3 6
19.4%
4 2
 
6.5%
5 1
 
3.2%
6 1
 
3.2%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 259
85.8%
Common 43
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 42
16.2%
S 39
15.1%
N 28
10.8%
C 26
10.0%
X 16
 
6.2%
T 13
 
5.0%
P 13
 
5.0%
J 11
 
4.2%
B 10
 
3.9%
H 10
 
3.9%
Other values (13) 51
19.7%
Common
ValueCountFrequency (%)
1 11
25.6%
2 10
23.3%
3 6
14.0%
( 6
14.0%
) 5
11.6%
4 2
 
4.7%
5 1
 
2.3%
- 1
 
2.3%
6 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 42
13.9%
S 39
12.9%
N 28
 
9.3%
C 26
 
8.6%
X 16
 
5.3%
T 13
 
4.3%
P 13
 
4.3%
J 11
 
3.6%
1 11
 
3.6%
B 10
 
3.3%
Other values (22) 93
30.8%

서비스명
Text

Distinct80
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-12T15:08:48.228014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length21.341463
Min length11

Characters and Unicode

Total characters1750
Distinct characters57
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

Unique78 ?
Unique (%)95.1%

Sample

1st rowAsia Europe 10
2nd rowFar east-India subcontinent 3
3rd rowIntra Asia 5
4th rowAFRICA Express
5th rowPacific North 4
ValueCountFrequency (%)
korea 37
 
13.4%
service 30
 
10.8%
china 19
 
6.9%
express 14
 
5.1%
japan 10
 
3.6%
new 9
 
3.2%
2 7
 
2.5%
asia 7
 
2.5%
vietnam 7
 
2.5%
east 6
 
2.2%
Other values (72) 131
47.3%
2023-12-12T15:08:48.745984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 201
 
11.5%
196
 
11.2%
e 160
 
9.1%
i 138
 
7.9%
n 112
 
6.4%
r 105
 
6.0%
o 89
 
5.1%
s 70
 
4.0%
h 60
 
3.4%
S 52
 
3.0%
Other values (47) 567
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1254
71.7%
Uppercase Letter 273
 
15.6%
Space Separator 196
 
11.2%
Decimal Number 23
 
1.3%
Dash Punctuation 3
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 201
16.0%
e 160
12.8%
i 138
11.0%
n 112
8.9%
r 105
8.4%
o 89
 
7.1%
s 70
 
5.6%
h 60
 
4.8%
t 49
 
3.9%
c 45
 
3.6%
Other values (14) 225
17.9%
Uppercase Letter
ValueCountFrequency (%)
S 52
19.0%
K 42
15.4%
C 23
8.4%
N 22
 
8.1%
E 19
 
7.0%
P 14
 
5.1%
A 14
 
5.1%
T 11
 
4.0%
H 10
 
3.7%
I 10
 
3.7%
Other values (13) 56
20.5%
Decimal Number
ValueCountFrequency (%)
2 8
34.8%
1 6
26.1%
3 4
17.4%
4 2
 
8.7%
5 1
 
4.3%
0 1
 
4.3%
6 1
 
4.3%
Space Separator
ValueCountFrequency (%)
196
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1527
87.3%
Common 223
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 201
13.2%
e 160
 
10.5%
i 138
 
9.0%
n 112
 
7.3%
r 105
 
6.9%
o 89
 
5.8%
s 70
 
4.6%
h 60
 
3.9%
S 52
 
3.4%
t 49
 
3.2%
Other values (37) 491
32.2%
Common
ValueCountFrequency (%)
196
87.9%
2 8
 
3.6%
1 6
 
2.7%
3 4
 
1.8%
- 3
 
1.3%
4 2
 
0.9%
5 1
 
0.4%
0 1
 
0.4%
& 1
 
0.4%
6 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 201
 
11.5%
196
 
11.2%
e 160
 
9.1%
i 138
 
7.9%
n 112
 
6.4%
r 105
 
6.0%
o 89
 
5.1%
s 70
 
4.0%
h 60
 
3.4%
S 52
 
3.0%
Other values (47) 567
32.4%

기항지1
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
Kwangyang
81 
Busan
 
1

Length

Max length9
Median length9
Mean length8.9512195
Min length5

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowKwangyang
2nd rowKwangyang
3rd rowKwangyang
4th rowKwangyang
5th rowKwangyang

Common Values

ValueCountFrequency (%)
Kwangyang 81
98.8%
Busan 1
 
1.2%

Length

2023-12-12T15:08:48.935329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:08:49.056000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kwangyang 81
98.8%
busan 1
 
1.2%

기항지2
Categorical

Distinct20
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
Busan
20 
Shanghai
13 
Ningbo
9 
Qingdao
8 
Ulsan
6 
Other values (15)
26 

Length

Max length11
Median length9
Mean length6.5365854
Min length5

Unique

Unique8 ?
Unique (%)9.8%

Sample

1st rowUlsan
2nd rowNingbo
3rd rowShanghai
4th rowNingbo
5th rowQingdao

Common Values

ValueCountFrequency (%)
Busan 20
24.4%
Shanghai 13
15.9%
Ningbo 9
11.0%
Qingdao 8
 
9.8%
Ulsan 6
 
7.3%
Keelung 4
 
4.9%
Hongkong 3
 
3.7%
Nanjing 3
 
3.7%
Lianyungang 2
 
2.4%
Dalian 2
 
2.4%
Other values (10) 12
14.6%

Length

2023-12-12T15:08:49.177819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
busan 20
24.1%
shanghai 13
15.7%
ningbo 9
10.8%
qingdao 8
 
9.6%
ulsan 6
 
7.2%
keelung 4
 
4.8%
hongkong 3
 
3.6%
nanjing 3
 
3.6%
yantai 2
 
2.4%
tianjin 2
 
2.4%
Other values (11) 13
15.7%

기항지3
Categorical

Distinct35
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Memory size788.0 B
Ningbo
12 
Shanghai
12 
Busan
9 
Qingdao
 
4
Taichung
 
3
Other values (30)
42 

Length

Max length22
Median length12
Mean length7.0853659
Min length2

Unique

Unique19 ?
Unique (%)23.2%

Sample

1st rowNingbo
2nd rowSingapore
3rd rowNingbo
4th rowNansha
5th rowNingbo

Common Values

ValueCountFrequency (%)
Ningbo 12
 
14.6%
Shanghai 12
 
14.6%
Busan 9
 
11.0%
Qingdao 4
 
4.9%
Taichung 3
 
3.7%
Hongkong 3
 
3.7%
Huangpu 2
 
2.4%
Tokyo 2
 
2.4%
Haiphong 2
 
2.4%
hochiminh 2
 
2.4%
Other values (25) 31
37.8%

Length

2023-12-12T15:08:49.337424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ningbo 12
 
14.5%
shanghai 12
 
14.5%
busan 9
 
10.8%
qingdao 4
 
4.8%
taichung 3
 
3.6%
hongkong 3
 
3.6%
zhangjiagang 3
 
3.6%
shekou 2
 
2.4%
hakata 2
 
2.4%
kaohsiung 2
 
2.4%
Other values (25) 31
37.3%

기항지4
Categorical

Distinct31
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Memory size788.0 B
Busan
22 
Shekou
7 
Hongkong
5 
Ulsan
 
4
Ningbo
 
4
Other values (26)
40 

Length

Max length15
Median length12
Mean length6.7804878
Min length4

Unique

Unique17 ?
Unique (%)20.7%

Sample

1st rowYantian
2nd rowTanjung Pelepas
3rd rowShekou
4th rowShekou
5th rowShanghai

Common Values

ValueCountFrequency (%)
Busan 22
26.8%
Shekou 7
 
8.5%
Hongkong 5
 
6.1%
Ulsan 4
 
4.9%
Ningbo 4
 
4.9%
Hochiminh 4
 
4.9%
Kaohsiung 4
 
4.9%
<NA> 3
 
3.7%
Surabaya 2
 
2.4%
Laemchabang 2
 
2.4%
Other values (21) 25
30.5%

Length

2023-12-12T15:08:49.475862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
busan 22
25.9%
shekou 7
 
8.2%
hongkong 5
 
5.9%
ulsan 4
 
4.7%
ningbo 4
 
4.7%
hochiminh 4
 
4.7%
kaohsiung 4
 
4.7%
na 3
 
3.5%
haiphong 2
 
2.4%
taipei 2
 
2.4%
Other values (24) 28
32.9%

기항지5
Text

MISSING 

Distinct32
Distinct (%)54.2%
Missing23
Missing (%)28.0%
Memory size788.0 B
2023-12-12T15:08:49.684326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length7.7118644
Min length4

Characters and Unicode

Total characters455
Distinct characters38
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

Unique17 ?
Unique (%)28.8%

Sample

1st rowTanjung Pelepas
2nd rowColombo
3rd rowTanjung Pelepas
4th rowSingapore
5th rowBusan
ValueCountFrequency (%)
busan 4
 
6.2%
shekou 4
 
6.2%
hochiminh 4
 
6.2%
bangkok 3
 
4.6%
singapore 3
 
4.6%
shimizu 3
 
4.6%
laemchabang 3
 
4.6%
ulsan 3
 
4.6%
port 3
 
4.6%
klang 3
 
4.6%
Other values (25) 32
49.2%
2023-12-12T15:08:50.071226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 60
13.2%
n 50
 
11.0%
o 40
 
8.8%
i 33
 
7.3%
h 30
 
6.6%
g 29
 
6.4%
u 19
 
4.2%
e 17
 
3.7%
k 17
 
3.7%
m 15
 
3.3%
Other values (28) 145
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 381
83.7%
Uppercase Letter 68
 
14.9%
Space Separator 6
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 60
15.7%
n 50
13.1%
o 40
10.5%
i 33
8.7%
h 30
7.9%
g 29
7.6%
u 19
 
5.0%
e 17
 
4.5%
k 17
 
4.5%
m 15
 
3.9%
Other values (11) 71
18.6%
Uppercase Letter
ValueCountFrequency (%)
S 13
19.1%
H 9
13.2%
T 7
10.3%
B 7
10.3%
P 7
10.3%
K 5
 
7.4%
N 4
 
5.9%
U 3
 
4.4%
L 3
 
4.4%
C 2
 
2.9%
Other values (6) 8
11.8%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 449
98.7%
Common 6
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 60
13.4%
n 50
 
11.1%
o 40
 
8.9%
i 33
 
7.3%
h 30
 
6.7%
g 29
 
6.5%
u 19
 
4.2%
e 17
 
3.8%
k 17
 
3.8%
m 15
 
3.3%
Other values (27) 139
31.0%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 60
13.2%
n 50
 
11.0%
o 40
 
8.8%
i 33
 
7.3%
h 30
 
6.6%
g 29
 
6.4%
u 19
 
4.2%
e 17
 
3.7%
k 17
 
3.7%
m 15
 
3.3%
Other values (28) 145
31.9%

기항지6
Text

MISSING 

Distinct34
Distinct (%)64.2%
Missing29
Missing (%)35.4%
Memory size788.0 B
2023-12-12T15:08:50.272810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length7.8113208
Min length4

Characters and Unicode

Total characters414
Distinct characters41
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

Unique23 ?
Unique (%)43.4%

Sample

1st rowSuez
2nd rowNhava Sheva
3rd rowYangon
4th rowColombo
5th rowPrince Rupert
ValueCountFrequency (%)
shekou 4
 
6.7%
laemchabang 4
 
6.7%
busan 4
 
6.7%
singapore 3
 
5.0%
bangkok 3
 
5.0%
klang 2
 
3.3%
nansha 2
 
3.3%
jakarta 2
 
3.3%
port 2
 
3.3%
hongkong 2
 
3.3%
Other values (30) 32
53.3%
2023-12-12T15:08:50.631643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 64
15.5%
n 46
 
11.1%
o 32
 
7.7%
g 27
 
6.5%
h 26
 
6.3%
e 24
 
5.8%
i 20
 
4.8%
k 19
 
4.6%
u 16
 
3.9%
S 13
 
3.1%
Other values (31) 127
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 348
84.1%
Uppercase Letter 59
 
14.3%
Space Separator 7
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 64
18.4%
n 46
13.2%
o 32
9.2%
g 27
7.8%
h 26
7.5%
e 24
 
6.9%
i 20
 
5.7%
k 19
 
5.5%
u 16
 
4.6%
c 12
 
3.4%
Other values (14) 62
17.8%
Uppercase Letter
ValueCountFrequency (%)
S 13
22.0%
B 7
11.9%
L 6
10.2%
P 6
10.2%
K 5
 
8.5%
H 4
 
6.8%
N 3
 
5.1%
Y 3
 
5.1%
J 2
 
3.4%
C 2
 
3.4%
Other values (6) 8
13.6%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 407
98.3%
Common 7
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 64
15.7%
n 46
 
11.3%
o 32
 
7.9%
g 27
 
6.6%
h 26
 
6.4%
e 24
 
5.9%
i 20
 
4.9%
k 19
 
4.7%
u 16
 
3.9%
S 13
 
3.2%
Other values (30) 120
29.5%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 64
15.5%
n 46
 
11.1%
o 32
 
7.7%
g 27
 
6.5%
h 26
 
6.3%
e 24
 
5.8%
i 20
 
4.8%
k 19
 
4.6%
u 16
 
3.9%
S 13
 
3.1%
Other values (31) 127
30.7%

기항지7
Text

MISSING 

Distinct33
Distinct (%)73.3%
Missing37
Missing (%)45.1%
Memory size788.0 B
2023-12-12T15:08:50.816214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length8.1333333
Min length4

Characters and Unicode

Total characters366
Distinct characters37
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

Unique26 ?
Unique (%)57.8%

Sample

1st rowAlgeciras
2nd rowPipavav
3rd rowTanjung Pelepas
4th rowLome
5th rowTacoma
ValueCountFrequency (%)
hochiminh 4
 
7.8%
port 3
 
5.9%
klang 3
 
5.9%
hongkong 3
 
5.9%
singapore 3
 
5.9%
busan 2
 
3.9%
laemchabang 2
 
3.9%
tanjung 2
 
3.9%
pelepas 2
 
3.9%
nhava 1
 
2.0%
Other values (26) 26
51.0%
2023-12-12T15:08:51.132272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 53
14.5%
n 40
 
10.9%
o 27
 
7.4%
e 25
 
6.8%
i 24
 
6.6%
g 22
 
6.0%
h 18
 
4.9%
H 11
 
3.0%
c 11
 
3.0%
l 10
 
2.7%
Other values (27) 125
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 309
84.4%
Uppercase Letter 51
 
13.9%
Space Separator 6
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 53
17.2%
n 40
12.9%
o 27
 
8.7%
e 25
 
8.1%
i 24
 
7.8%
g 22
 
7.1%
h 18
 
5.8%
c 11
 
3.6%
l 10
 
3.2%
m 10
 
3.2%
Other values (13) 69
22.3%
Uppercase Letter
ValueCountFrequency (%)
H 11
21.6%
S 8
15.7%
P 7
13.7%
K 5
9.8%
N 4
 
7.8%
T 4
 
7.8%
L 3
 
5.9%
B 3
 
5.9%
M 2
 
3.9%
I 1
 
2.0%
Other values (3) 3
 
5.9%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360
98.4%
Common 6
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 53
14.7%
n 40
 
11.1%
o 27
 
7.5%
e 25
 
6.9%
i 24
 
6.7%
g 22
 
6.1%
h 18
 
5.0%
H 11
 
3.1%
c 11
 
3.1%
l 10
 
2.8%
Other values (26) 119
33.1%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 53
14.5%
n 40
 
10.9%
o 27
 
7.4%
e 25
 
6.8%
i 24
 
6.6%
g 22
 
6.0%
h 18
 
4.9%
H 11
 
3.0%
c 11
 
3.0%
l 10
 
2.7%
Other values (27) 125
34.2%

기항지8
Text

MISSING 

Distinct27
Distinct (%)67.5%
Missing42
Missing (%)51.2%
Memory size788.0 B
2023-12-12T15:08:51.323782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.5
Min length4

Characters and Unicode

Total characters300
Distinct characters37
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

Unique18 ?
Unique (%)45.0%

Sample

1st rowBremerhaven
2nd rowSingapore
3rd rowMuara
4th rowDurban
5th rowVancouver
ValueCountFrequency (%)
incheon 4
 
9.3%
hongkong 4
 
9.3%
shanghai 2
 
4.7%
hakata 2
 
4.7%
hochiminh 2
 
4.7%
vancouver 2
 
4.7%
singapore 2
 
4.7%
xiamen 2
 
4.7%
kwangyang 2
 
4.7%
ulsan 1
 
2.3%
Other values (20) 20
46.5%
2023-12-12T15:08:51.653642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 43
14.3%
n 39
13.0%
o 26
 
8.7%
g 19
 
6.3%
h 18
 
6.0%
i 17
 
5.7%
e 17
 
5.7%
r 12
 
4.0%
c 9
 
3.0%
u 9
 
3.0%
Other values (27) 91
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 254
84.7%
Uppercase Letter 43
 
14.3%
Space Separator 3
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 43
16.9%
n 39
15.4%
o 26
10.2%
g 19
7.5%
h 18
 
7.1%
i 17
 
6.7%
e 17
 
6.7%
r 12
 
4.7%
c 9
 
3.5%
u 9
 
3.5%
Other values (11) 45
17.7%
Uppercase Letter
ValueCountFrequency (%)
K 8
18.6%
H 8
18.6%
S 6
14.0%
I 4
9.3%
L 2
 
4.7%
M 2
 
4.7%
D 2
 
4.7%
B 2
 
4.7%
X 2
 
4.7%
V 2
 
4.7%
Other values (5) 5
11.6%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 297
99.0%
Common 3
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 43
14.5%
n 39
13.1%
o 26
 
8.8%
g 19
 
6.4%
h 18
 
6.1%
i 17
 
5.7%
e 17
 
5.7%
r 12
 
4.0%
c 9
 
3.0%
u 9
 
3.0%
Other values (26) 88
29.6%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 43
14.3%
n 39
13.0%
o 26
 
8.7%
g 19
 
6.3%
h 18
 
6.0%
i 17
 
5.7%
e 17
 
5.7%
r 12
 
4.0%
c 9
 
3.0%
u 9
 
3.0%
Other values (27) 91
30.3%

기항지9
Text

MISSING 

Distinct24
Distinct (%)80.0%
Missing52
Missing (%)63.4%
Memory size788.0 B
2023-12-12T15:08:51.840439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10.5
Mean length6.8666667
Min length4

Characters and Unicode

Total characters206
Distinct characters36
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

Unique23 ?
Unique (%)76.7%

Sample

1st rowRotterdam
2nd rowDalian
3rd rowTawau
4th rowPort Louis
5th rowBusan
ValueCountFrequency (%)
busan 7
20.6%
port 2
 
5.9%
karachi 1
 
2.9%
nhava 1
 
2.9%
rotterdam 1
 
2.9%
ningbo 1
 
2.9%
mundra 1
 
2.9%
taipei 1
 
2.9%
manila 1
 
2.9%
moji 1
 
2.9%
Other values (17) 17
50.0%
2023-12-12T15:08:52.195543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 35
17.0%
n 25
 
12.1%
i 14
 
6.8%
o 13
 
6.3%
u 12
 
5.8%
s 10
 
4.9%
e 10
 
4.9%
h 8
 
3.9%
g 8
 
3.9%
B 7
 
3.4%
Other values (26) 64
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 168
81.6%
Uppercase Letter 34
 
16.5%
Space Separator 4
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 35
20.8%
n 25
14.9%
i 14
 
8.3%
o 13
 
7.7%
u 12
 
7.1%
s 10
 
6.0%
e 10
 
6.0%
h 8
 
4.8%
g 8
 
4.8%
t 6
 
3.6%
Other values (12) 27
16.1%
Uppercase Letter
ValueCountFrequency (%)
B 7
20.6%
K 4
11.8%
M 4
11.8%
P 4
11.8%
T 3
8.8%
N 3
8.8%
S 3
8.8%
I 1
 
2.9%
R 1
 
2.9%
L 1
 
2.9%
Other values (3) 3
8.8%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 202
98.1%
Common 4
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 35
17.3%
n 25
12.4%
i 14
 
6.9%
o 13
 
6.4%
u 12
 
5.9%
s 10
 
5.0%
e 10
 
5.0%
h 8
 
4.0%
g 8
 
4.0%
B 7
 
3.5%
Other values (25) 60
29.7%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 35
17.0%
n 25
 
12.1%
i 14
 
6.8%
o 13
 
6.3%
u 12
 
5.8%
s 10
 
4.9%
e 10
 
4.9%
h 8
 
3.9%
g 8
 
3.9%
B 7
 
3.4%
Other values (26) 64
31.1%

기항지10
Text

MISSING 

Distinct16
Distinct (%)76.2%
Missing61
Missing (%)74.4%
Memory size788.0 B
2023-12-12T15:08:52.347614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.7619048
Min length4

Characters and Unicode

Total characters142
Distinct characters34
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

Unique13 ?
Unique (%)61.9%

Sample

1st rowSuez
2nd rowXingang
3rd rowDavao City
4th rowColombo
5th rowBusan
ValueCountFrequency (%)
busan 4
16.7%
klang 2
 
8.3%
ulsan 2
 
8.3%
port 2
 
8.3%
taichung 1
 
4.2%
suez 1
 
4.2%
karachi 1
 
4.2%
hakata 1
 
4.2%
kaohsiung 1
 
4.2%
osaka 1
 
4.2%
Other values (8) 8
33.3%
2023-12-12T15:08:52.649595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 24
16.9%
n 15
 
10.6%
o 10
 
7.0%
s 9
 
6.3%
u 9
 
6.3%
i 8
 
5.6%
g 7
 
4.9%
h 6
 
4.2%
l 5
 
3.5%
r 5
 
3.5%
Other values (24) 44
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 115
81.0%
Uppercase Letter 24
 
16.9%
Space Separator 3
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 24
20.9%
n 15
13.0%
o 10
8.7%
s 9
 
7.8%
u 9
 
7.8%
i 8
 
7.0%
g 7
 
6.1%
h 6
 
5.2%
l 5
 
4.3%
r 5
 
4.3%
Other values (10) 17
14.8%
Uppercase Letter
ValueCountFrequency (%)
B 4
16.7%
K 4
16.7%
C 3
12.5%
P 2
8.3%
U 2
8.3%
S 2
8.3%
D 1
 
4.2%
M 1
 
4.2%
H 1
 
4.2%
O 1
 
4.2%
Other values (3) 3
12.5%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 139
97.9%
Common 3
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 24
17.3%
n 15
 
10.8%
o 10
 
7.2%
s 9
 
6.5%
u 9
 
6.5%
i 8
 
5.8%
g 7
 
5.0%
h 6
 
4.3%
l 5
 
3.6%
r 5
 
3.6%
Other values (23) 41
29.5%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 24
16.9%
n 15
 
10.6%
o 10
 
7.0%
s 9
 
6.3%
u 9
 
6.3%
i 8
 
5.6%
g 7
 
4.9%
h 6
 
4.2%
l 5
 
3.5%
r 5
 
3.5%
Other values (24) 44
31.0%

기항지11
Text

MISSING 

Distinct16
Distinct (%)88.9%
Missing64
Missing (%)78.0%
Memory size788.0 B
2023-12-12T15:08:52.812539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length8.1666667
Min length4

Characters and Unicode

Total characters147
Distinct characters36
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

Unique14 ?
Unique (%)77.8%

Sample

1st rowTanjung Pelepas
2nd rowQingdao
3rd rowCagayan De Oro
4th rowSingapore
5th rowUlsan
ValueCountFrequency (%)
busan 2
 
8.0%
ulsan 2
 
8.0%
taipei 1
 
4.0%
pelepas 1
 
4.0%
tanjung 1
 
4.0%
hongkong 1
 
4.0%
klang 1
 
4.0%
port 1
 
4.0%
incheon 1
 
4.0%
gudang 1
 
4.0%
Other values (13) 13
52.0%
2023-12-12T15:08:53.131975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 23
15.6%
n 17
 
11.6%
o 10
 
6.8%
g 9
 
6.1%
i 9
 
6.1%
7
 
4.8%
s 7
 
4.8%
e 7
 
4.8%
l 5
 
3.4%
r 5
 
3.4%
Other values (26) 48
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 116
78.9%
Uppercase Letter 24
 
16.3%
Space Separator 7
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 23
19.8%
n 17
14.7%
o 10
8.6%
g 9
 
7.8%
i 9
 
7.8%
s 7
 
6.0%
e 7
 
6.0%
l 5
 
4.3%
r 5
 
4.3%
u 4
 
3.4%
Other values (11) 20
17.2%
Uppercase Letter
ValueCountFrequency (%)
P 4
16.7%
B 3
12.5%
K 3
12.5%
T 2
8.3%
S 2
8.3%
U 2
8.3%
O 1
 
4.2%
W 1
 
4.2%
Q 1
 
4.2%
D 1
 
4.2%
Other values (4) 4
16.7%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 140
95.2%
Common 7
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 23
16.4%
n 17
 
12.1%
o 10
 
7.1%
g 9
 
6.4%
i 9
 
6.4%
s 7
 
5.0%
e 7
 
5.0%
l 5
 
3.6%
r 5
 
3.6%
P 4
 
2.9%
Other values (25) 44
31.4%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 23
15.6%
n 17
 
11.6%
o 10
 
6.8%
g 9
 
6.1%
i 9
 
6.1%
7
 
4.8%
s 7
 
4.8%
e 7
 
4.8%
l 5
 
3.4%
r 5
 
3.4%
Other values (26) 48
32.7%

기항지12
Text

MISSING 

Distinct11
Distinct (%)73.3%
Missing67
Missing (%)81.7%
Memory size788.0 B
2023-12-12T15:08:53.299215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length8
Mean length7.0666667
Min length4

Characters and Unicode

Total characters106
Distinct characters28
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

Unique8 ?
Unique (%)53.3%

Sample

1st rowShanghai
2nd rowBusan
3rd rowShanghai
4th rowTianjin
5th rowBusan
ValueCountFrequency (%)
busan 3
18.8%
shanghai 2
12.5%
singapore 2
12.5%
tianjin 1
 
6.2%
tanjung 1
 
6.2%
pelepas 1
 
6.2%
ulsan 1
 
6.2%
qingdao 1
 
6.2%
moji 1
 
6.2%
incheon 1
 
6.2%
Other values (2) 2
12.5%
2023-12-12T15:08:53.644768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 18
17.0%
n 15
14.2%
i 8
 
7.5%
e 6
 
5.7%
h 6
 
5.7%
g 6
 
5.7%
s 5
 
4.7%
o 5
 
4.7%
S 4
 
3.8%
u 4
 
3.8%
Other values (18) 29
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 89
84.0%
Uppercase Letter 16
 
15.1%
Space Separator 1
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18
20.2%
n 15
16.9%
i 8
9.0%
e 6
 
6.7%
h 6
 
6.7%
g 6
 
6.7%
s 5
 
5.6%
o 5
 
5.6%
u 4
 
4.5%
d 3
 
3.4%
Other values (7) 13
14.6%
Uppercase Letter
ValueCountFrequency (%)
S 4
25.0%
B 3
18.8%
T 2
12.5%
P 1
 
6.2%
U 1
 
6.2%
Q 1
 
6.2%
M 1
 
6.2%
I 1
 
6.2%
H 1
 
6.2%
J 1
 
6.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 105
99.1%
Common 1
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18
17.1%
n 15
14.3%
i 8
 
7.6%
e 6
 
5.7%
h 6
 
5.7%
g 6
 
5.7%
s 5
 
4.8%
o 5
 
4.8%
S 4
 
3.8%
u 4
 
3.8%
Other values (17) 28
26.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18
17.0%
n 15
14.2%
i 8
 
7.5%
e 6
 
5.7%
h 6
 
5.7%
g 6
 
5.7%
s 5
 
4.7%
o 5
 
4.7%
S 4
 
3.8%
u 4
 
3.8%
Other values (18) 29
27.4%

기항지13
Text

MISSING 

Distinct8
Distinct (%)72.7%
Missing71
Missing (%)86.6%
Memory size788.0 B
2023-12-12T15:08:53.802092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.7272727
Min length4

Characters and Unicode

Total characters63
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)63.6%

Sample

1st rowXingang
2nd rowDalian
3rd rowBusan
4th rowUlsan
5th rowQingdao
ValueCountFrequency (%)
busan 4
36.4%
xingang 1
 
9.1%
dalian 1
 
9.1%
ulsan 1
 
9.1%
qingdao 1
 
9.1%
hongkong 1
 
9.1%
dachan 1
 
9.1%
suez 1
 
9.1%
2023-12-12T15:08:54.147175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 12
19.0%
a 11
17.5%
s 5
7.9%
g 5
7.9%
u 5
7.9%
B 4
 
6.3%
i 3
 
4.8%
o 3
 
4.8%
D 2
 
3.2%
l 2
 
3.2%
Other values (11) 11
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52
82.5%
Uppercase Letter 11
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 12
23.1%
a 11
21.2%
s 5
9.6%
g 5
9.6%
u 5
9.6%
i 3
 
5.8%
o 3
 
5.8%
l 2
 
3.8%
k 1
 
1.9%
e 1
 
1.9%
Other values (4) 4
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
B 4
36.4%
D 2
18.2%
S 1
 
9.1%
U 1
 
9.1%
H 1
 
9.1%
Q 1
 
9.1%
X 1
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 63
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 12
19.0%
a 11
17.5%
s 5
7.9%
g 5
7.9%
u 5
7.9%
B 4
 
6.3%
i 3
 
4.8%
o 3
 
4.8%
D 2
 
3.2%
l 2
 
3.2%
Other values (11) 11
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 12
19.0%
a 11
17.5%
s 5
7.9%
g 5
7.9%
u 5
7.9%
B 4
 
6.3%
i 3
 
4.8%
o 3
 
4.8%
D 2
 
3.2%
l 2
 
3.2%
Other values (11) 11
17.5%

기항지14
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing77
Missing (%)93.9%
Memory size788.0 B
2023-12-12T15:08:54.324289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8
Min length7

Characters and Unicode

Total characters40
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowQingdao
2nd rowIncheon
3rd rowKaohsiung
4th rowHongkong
5th rowDamaietta
ValueCountFrequency (%)
qingdao 1
20.0%
incheon 1
20.0%
kaohsiung 1
20.0%
hongkong 1
20.0%
damaietta 1
20.0%
2023-12-12T15:08:54.666218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 6
15.0%
a 5
12.5%
o 5
12.5%
g 4
10.0%
i 3
 
7.5%
h 2
 
5.0%
e 2
 
5.0%
t 2
 
5.0%
c 1
 
2.5%
I 1
 
2.5%
Other values (9) 9
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35
87.5%
Uppercase Letter 5
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 6
17.1%
a 5
14.3%
o 5
14.3%
g 4
11.4%
i 3
8.6%
h 2
 
5.7%
e 2
 
5.7%
t 2
 
5.7%
c 1
 
2.9%
d 1
 
2.9%
Other values (4) 4
11.4%
Uppercase Letter
ValueCountFrequency (%)
I 1
20.0%
K 1
20.0%
H 1
20.0%
D 1
20.0%
Q 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 6
15.0%
a 5
12.5%
o 5
12.5%
g 4
10.0%
i 3
 
7.5%
h 2
 
5.0%
e 2
 
5.0%
t 2
 
5.0%
c 1
 
2.5%
I 1
 
2.5%
Other values (9) 9
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 6
15.0%
a 5
12.5%
o 5
12.5%
g 4
10.0%
i 3
 
7.5%
h 2
 
5.0%
e 2
 
5.0%
t 2
 
5.0%
c 1
 
2.5%
I 1
 
2.5%
Other values (9) 9
22.5%

기항지15
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing79
Missing (%)96.3%
Memory size788.0 B
2023-12-12T15:08:54.823962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6
Min length5

Characters and Unicode

Total characters18
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowBusan
2nd rowTaipei
3rd rowPiraeus
ValueCountFrequency (%)
busan 1
33.3%
taipei 1
33.3%
piraeus 1
33.3%
2023-12-12T15:08:55.451262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
16.7%
i 3
16.7%
u 2
11.1%
s 2
11.1%
e 2
11.1%
B 1
 
5.6%
n 1
 
5.6%
T 1
 
5.6%
p 1
 
5.6%
P 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15
83.3%
Uppercase Letter 3
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3
20.0%
i 3
20.0%
u 2
13.3%
s 2
13.3%
e 2
13.3%
n 1
 
6.7%
p 1
 
6.7%
r 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 1
33.3%
T 1
33.3%
P 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 18
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3
16.7%
i 3
16.7%
u 2
11.1%
s 2
11.1%
e 2
11.1%
B 1
 
5.6%
n 1
 
5.6%
T 1
 
5.6%
p 1
 
5.6%
P 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3
16.7%
i 3
16.7%
u 2
11.1%
s 2
11.1%
e 2
11.1%
B 1
 
5.6%
n 1
 
5.6%
T 1
 
5.6%
p 1
 
5.6%
P 1
 
5.6%

기항지16
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing79
Missing (%)96.3%
Memory size788.0 B
2023-12-12T15:08:55.622196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.6666667
Min length5

Characters and Unicode

Total characters17
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowTokyo
2nd rowIncheon
3rd rowGenoa
ValueCountFrequency (%)
tokyo 1
33.3%
incheon 1
33.3%
genoa 1
33.3%
2023-12-12T15:08:55.942252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4
23.5%
n 3
17.6%
e 2
11.8%
T 1
 
5.9%
k 1
 
5.9%
y 1
 
5.9%
I 1
 
5.9%
c 1
 
5.9%
h 1
 
5.9%
G 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14
82.4%
Uppercase Letter 3
 
17.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4
28.6%
n 3
21.4%
e 2
14.3%
k 1
 
7.1%
y 1
 
7.1%
c 1
 
7.1%
h 1
 
7.1%
a 1
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
I 1
33.3%
G 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 17
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4
23.5%
n 3
17.6%
e 2
11.8%
T 1
 
5.9%
k 1
 
5.9%
y 1
 
5.9%
I 1
 
5.9%
c 1
 
5.9%
h 1
 
5.9%
G 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4
23.5%
n 3
17.6%
e 2
11.8%
T 1
 
5.9%
k 1
 
5.9%
y 1
 
5.9%
I 1
 
5.9%
c 1
 
5.9%
h 1
 
5.9%
G 1
 
5.9%

기항지17
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing81
Missing (%)98.8%
Memory size788.0 B
2023-12-12T15:08:56.083825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowValencia
ValueCountFrequency (%)
valencia 1
100.0%
2023-12-12T15:08:56.353731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
25.0%
V 1
12.5%
l 1
12.5%
e 1
12.5%
n 1
12.5%
c 1
12.5%
i 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7
87.5%
Uppercase Letter 1
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2
28.6%
l 1
14.3%
e 1
14.3%
n 1
14.3%
c 1
14.3%
i 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
V 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2
25.0%
V 1
12.5%
l 1
12.5%
e 1
12.5%
n 1
12.5%
c 1
12.5%
i 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2
25.0%
V 1
12.5%
l 1
12.5%
e 1
12.5%
n 1
12.5%
c 1
12.5%
i 1
12.5%

선박톤수(teu)
Real number (ℝ)

Distinct36
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2664.7073
Minimum120
Maximum18000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T15:08:56.495595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile411
Q1847.5
median1450
Q32500
95-th percentile10900
Maximum18000
Range17880
Interquartile range (IQR)1652.5

Descriptive statistics

Standard deviation3579.3933
Coefficient of variation (CV)1.3432595
Kurtosis7.9382798
Mean2664.7073
Median Absolute Deviation (MAD)750
Skewness2.7536536
Sum218506
Variance12812056
MonotonicityNot monotonic
2023-12-12T15:08:56.619717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
700 13
15.9%
1000 12
14.6%
1700 9
 
11.0%
900 4
 
4.9%
2500 3
 
3.7%
1600 3
 
3.7%
1800 3
 
3.7%
370 3
 
3.7%
11000 2
 
2.4%
2000 2
 
2.4%
Other values (26) 28
34.1%
ValueCountFrequency (%)
120 1
 
1.2%
370 3
 
3.7%
400 1
 
1.2%
620 1
 
1.2%
630 1
 
1.2%
700 13
15.9%
830 1
 
1.2%
900 4
 
4.9%
950 1
 
1.2%
1000 12
14.6%
ValueCountFrequency (%)
18000 2
2.4%
13000 1
1.2%
11000 2
2.4%
9000 1
1.2%
8300 1
1.2%
8000 1
1.2%
7200 1
1.2%
6500 1
1.2%
5500 1
1.2%
5400 1
1.2%

선박투입선사(선박수)
Text

Distinct65
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-12T15:08:56.867691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length6
Mean length10.5
Min length6

Characters and Unicode

Total characters861
Distinct characters32
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

Unique56 ?
Unique (%)68.3%

Sample

1st rowMAE(13)
2nd rowMAE(7)
3rd rowMCC(7)
4th rowMSC(11)
5th rowHLC(3), ONE(1), HMM(3)
ValueCountFrequency (%)
nsl(1 9
 
7.2%
skr(2 8
 
6.4%
skr(1 8
 
6.4%
kmd(1 8
 
6.4%
pol(1 7
 
5.6%
has(1 7
 
5.6%
kmd(2 5
 
4.0%
eas(1 5
 
4.0%
tsl(1 4
 
3.2%
ckl(1 3
 
2.4%
Other values (48) 61
48.8%
2023-12-12T15:08:57.266156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 128
14.9%
) 128
14.9%
1 85
9.9%
S 66
 
7.7%
L 48
 
5.6%
M 46
 
5.3%
, 46
 
5.3%
44
 
5.1%
K 40
 
4.6%
D 23
 
2.7%
Other values (22) 207
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 384
44.6%
Decimal Number 131
 
15.2%
Open Punctuation 128
 
14.9%
Close Punctuation 128
 
14.9%
Other Punctuation 46
 
5.3%
Space Separator 44
 
5.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 66
17.2%
L 48
12.5%
M 46
12.0%
K 40
10.4%
D 23
 
6.0%
C 21
 
5.5%
H 21
 
5.5%
A 20
 
5.2%
N 19
 
4.9%
R 17
 
4.4%
Other values (11) 63
16.4%
Decimal Number
ValueCountFrequency (%)
1 85
64.9%
2 19
 
14.5%
3 11
 
8.4%
4 7
 
5.3%
7 4
 
3.1%
5 3
 
2.3%
6 2
 
1.5%
Open Punctuation
ValueCountFrequency (%)
( 128
100.0%
Close Punctuation
ValueCountFrequency (%)
) 128
100.0%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%
Space Separator
ValueCountFrequency (%)
44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 477
55.4%
Latin 384
44.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 66
17.2%
L 48
12.5%
M 46
12.0%
K 40
10.4%
D 23
 
6.0%
C 21
 
5.5%
H 21
 
5.5%
A 20
 
5.2%
N 19
 
4.9%
R 17
 
4.4%
Other values (11) 63
16.4%
Common
ValueCountFrequency (%)
( 128
26.8%
) 128
26.8%
1 85
17.8%
, 46
 
9.6%
44
 
9.2%
2 19
 
4.0%
3 11
 
2.3%
4 7
 
1.5%
7 4
 
0.8%
5 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 128
14.9%
) 128
14.9%
1 85
9.9%
S 66
 
7.7%
L 48
 
5.6%
M 46
 
5.3%
, 46
 
5.3%
44
 
5.1%
K 40
 
4.6%
D 23
 
2.7%
Other values (22) 207
24.0%

기항요일
Categorical

Distinct7
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size788.0 B
THU
19 
SUN
14 
FRI
13 
SAT
13 
WED
11 
Other values (2)
12 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUN
2nd rowTUE
3rd rowFRI
4th rowTUE
5th rowTHU

Common Values

ValueCountFrequency (%)
THU 19
23.2%
SUN 14
17.1%
FRI 13
15.9%
SAT 13
15.9%
WED 11
13.4%
TUE 7
 
8.5%
MON 5
 
6.1%

Length

2023-12-12T15:08:57.410727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:08:57.542661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
thu 19
23.2%
sun 14
17.1%
fri 13
15.9%
sat 13
15.9%
wed 11
13.4%
tue 7
 
8.5%
mon 5
 
6.1%

선박수
Real number (ℝ)

Distinct11
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2073171
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T15:08:57.666786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile7.95
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5902848
Coefficient of variation (CV)0.80761732
Kurtosis5.4684997
Mean3.2073171
Median Absolute Deviation (MAD)1
Skewness2.1073319
Sum263
Variance6.7095754
MonotonicityNot monotonic
2023-12-12T15:08:57.836467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 23
28.0%
2 16
19.5%
3 16
19.5%
4 14
17.1%
7 4
 
4.9%
8 2
 
2.4%
6 2
 
2.4%
5 2
 
2.4%
13 1
 
1.2%
11 1
 
1.2%
ValueCountFrequency (%)
1 23
28.0%
2 16
19.5%
3 16
19.5%
4 14
17.1%
5 2
 
2.4%
6 2
 
2.4%
7 4
 
4.9%
8 2
 
2.4%
11 1
 
1.2%
13 1
 
1.2%
ValueCountFrequency (%)
14 1
 
1.2%
13 1
 
1.2%
11 1
 
1.2%
8 2
 
2.4%
7 4
 
4.9%
6 2
 
2.4%
5 2
 
2.4%
4 14
17.1%
3 16
19.5%
2 16
19.5%

선박투입선사수
Categorical

Distinct5
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size788.0 B
1
45 
2
21 
3
10 
4
5 
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 45
54.9%
2 21
25.6%
3 10
 
12.2%
4 5
 
6.1%
6 1
 
1.2%

Length

2023-12-12T15:08:57.960939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:08:58.099063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 45
54.9%
2 21
25.6%
3 10
 
12.2%
4 5
 
6.1%
6 1
 
1.2%

Sample

터미널터미널별항차수선사(코드)국적_외국적 구분얼라이언스명기항선사코드공동운항등선사코드1공동운항등선사코드2공동운항등선사코드3공동운항등선사코드4공동운항등선사코드5서비스지역서비스코드서비스명기항지1기항지2기항지3기항지4기항지5기항지6기항지7기항지8기항지9기항지10기항지11기항지12기항지13기항지14기항지15기항지16기항지17선박톤수(teu)선박투입선사(선박수)기항요일선박수선박투입선사수
0GWCT1머스크라인(MAE)외국적2MMAEMSC<NA><NA><NA><NA>유럽NEU2Asia Europe 10KwangyangUlsanNingboYantianTanjung PelepasSuezAlgecirasBremerhavenRotterdamSuezTanjung PelepasShanghaiXingangQingdao<NA><NA><NA>18000MAE(13)SUN132
1GWCT2머스크라인(MAE)외국적<NA>MAECMAAPLOOCLZIM<NA>중동FI3Far east-India subcontinent 3KwangyangNingboSingaporeTanjung PelepasColomboNhava ShevaPipavavSingaporeDalianXingangQingdaoBusan<NA><NA><NA><NA><NA>9000MAE(7)TUE71
2GWCT3MCC외국적<NA>MCC<NA><NA><NA><NA><NA>동남아IA5Intra Asia 5KwangyangShanghaiNingboShekouTanjung PelepasYangonTanjung PelepasMuaraTawauDavao CityCagayan De OroShanghaiDalianIncheonBusanTokyo<NA>1700MCC(7)FRI81
3GWCT4MSC외국적<NA>MSC<NA><NA><NA><NA><NA>아프리카AFRICAAFRICA ExpressKwangyangNingboNanshaShekouSingaporeColomboLomeDurbanPort LouisColomboSingaporeTianjinBusan<NA><NA><NA><NA>13000MSC(11)TUE111
4GWCT5HMM국적THE AHMMHLCONE<NA><NA><NA>북미PN4Pacific North 4KwangyangQingdaoNingboShanghaiBusanPrince RupertTacomaVancouverBusan<NA><NA><NA><NA><NA><NA><NA><NA>7200HLC(3), ONE(1), HMM(3)THU73
5GWCT6CNC외국적<NA>CNC<NA><NA><NA><NA><NA>동남아JTVSJapan Thailand Vietnam ServiceKwangyangChu LaiLaemchabangHochiminhTokyoYokohamaNagoyaKobeBusan<NA><NA><NA><NA><NA><NA><NA><NA>1800CNC(4)FRI41
6GWCT7양밍라인(YML)외국적<NA>YMLTSL<NA><NA><NA><NA>동북아PASPan Asia ServiceKwangyangKeelungTaichungKaohsiungHongkongShekouMojiHakataBusan<NA><NA><NA><NA><NA><NA><NA><NA>1800YML(1), TSL(1)THU22
7GWCT8남성해운(NSL)국적<NA>NSLKMD<NA><NA><NA><NA>동북아NTPNorth China PendulumKwangyangShanghaiNingboBusanShimizuSendaiHachinoheTomakomaiSakai MinatoBusanUlsan<NA><NA><NA><NA><NA><NA>700NSL(2), KMD(1)WED32
8GWCT9고려해운(KMD)국적<NA>KMDNSL<NA><NA><NA><NA>동북아NSPNew Shanghai PendulumKwangyangNingboShanghaiBusanShimizuHitachinakaSendaiKushiroTomakomaiIshikariSakai minatoBusanUlsan<NA><NA><NA><NA>900KMD(2), NSL(1)SUN32
9GWCT10남성해운(NSL)국적<NA>NSLKMD<NA><NA><NA><NA>동북아NCQNorth China QingdaoKwangyangBusanKanazawaNiigataTomakomaiSendaiHitachinakaKwangyang<NA><NA><NA><NA><NA><NA><NA><NA><NA>1000NSL(3), KMD(2)SUN32
터미널터미널별항차수선사(코드)국적_외국적 구분얼라이언스명기항선사코드공동운항등선사코드1공동운항등선사코드2공동운항등선사코드3공동운항등선사코드4공동운항등선사코드5서비스지역서비스코드서비스명기항지1기항지2기항지3기항지4기항지5기항지6기항지7기항지8기항지9기항지10기항지11기항지12기항지13기항지14기항지15기항지16기항지17선박톤수(teu)선박투입선사(선박수)기항요일선박수선박투입선사수
72KIT28남성해운(NSL)국적<NA>NSLCKLPCLSML<NA><NA>동남아KVTKorea Vietnam Thailand ServiceKwangyangBusanHongkongHochiminhLaemchabangBangkokLaemchabangHochiminh<NA><NA><NA><NA><NA><NA><NA><NA><NA>1800NSL(1), CKL(1), PCL(1)FRI34
73KIT29남성해운(NSL)국적<NA>NSLKMDCKLPCL<NA><NA>동북아NS3NCS Shuttle 3KwangyangShanghaiNingboBusanUlsan<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1000NSL(1)SUN14
74KIT30남성해운(NSL)국적<NA>NSLKMD<NA><NA><NA><NA>동북아NBP(W)New Bohai PendulumKwangyangQingdaoDalianBusanUlsanKwangyang<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1000NSL(3), KMD(2)THU52
75KIT31SCL국적<NA>SCL<NA><NA><NA><NA><NA>연안KDSKwangyang Donghae ServiceKwangyangDonghae<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>120SCL(2)WED21
76KIT32고려해운(KMD)외국적<NA>KMD<NA><NA><NA><NA><NA>동남아PKSPhilippine Korea ServiceKwangyangBusanPohangManilaHochiminhHongkongShekouIncheon<NA><NA><NA><NA><NA><NA><NA><NA><NA>1600KMD(2), HAS(2)WED42
77KIT33HMM국적<NA>HMMONE<NA><NA><NA><NA>북미PSXWPacific South ExpressKwangyangIncheonShanghai<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>11000HMM(7)SAT71
78KIT34HMM국적<NA>HMMONE<NA><NA><NA><NA>북미PSXEPacific South ExpressKwangyangBusanLALong BeachOaklandBusan<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>11000HMM(7)FRI71
79KIT35에버그린(EMC)외국적<NA>EMC<NA><NA><NA><NA><NA>동남아KTH1Korea Taiwan HaiphongKwangyangQingdaoTaipeiKaohsiungHaiphongKaohsiungHaiphongKaohsiungTaipeiHakataUlsanBusan<NA><NA><NA><NA><NA>2000EMC(3)SAT31
80KIT36HMM국적<NA>HMM<NA><NA><NA><NA><NA>중동CIXPacific South India ExpressKwangyangShanghaiNingboKaohsiungShekouSingaporePort KlangNhava ShevaMundraKarachiPort KlangSingaporeDachanHongkong<NA><NA><NA>8000HMM(6)THU61
81KIT37HMM국적<NA>HMM<NA><NA><NA><NA><NA>유럽FIMFar East India MediterraneanBusanKwangyangShanghaiNingboShekouSingaporePort KlangKattupalliNhava ShevaMundraKarachiJeddahSuezDamaiettaPiraeusGenoaValencia18000HMM(1)WED11