Overview

Dataset statistics

Number of variables5
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory45.5 B

Variable types

Text2
Categorical1
Numeric2

Dataset

Description광양항을 이용하는 컨테이너선의 기항 현황(선사별 항차)데이터 입니다. 선사명, 선사코드, 국적 외국적 구분, 주당항차수, 공동운항항차수로 구성되어 있습니다. 소수점 입력이 불가하여 2주에 1번 입항건에 대하여 반올림하여 수량을 올립니다. 데이터는 연간 갱신됩니다.
Author여수광양항만공사
URLhttps://www.data.go.kr/data/15060307/fileData.do

Alerts

주당항차수 is highly overall correlated with 공동운항포함주당항차수High correlation
공동운항포함주당항차수 is highly overall correlated with 주당항차수 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 공동운항포함주당항차수High correlation
코드 has unique valuesUnique
주당항차수 has 13 (34.2%) zerosZeros
공동운항포함주당항차수 has 2 (5.3%) zerosZeros

Reproduction

Analysis started2023-12-12 00:38:46.784110
Analysis finished2023-12-12 00:38:47.598130
Duration0.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

선사별
Text

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T09:38:47.795899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length7.5789474
Min length3

Characters and Unicode

Total characters288
Distinct characters89
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)94.7%

Sample

1st rowSM상선(SML)
2nd row장금상선(SKR)
3rd row흥아라인(HAS)
4th rowHMM
5th row고려해운(KMD)
ValueCountFrequency (%)
zim 2
 
4.8%
line 2
 
4.8%
esl 1
 
2.4%
cnc 1
 
2.4%
ts라인(tsl 1
 
2.4%
cma-cgm 1
 
2.4%
에버그린(emc 1
 
2.4%
시노트란스(snt 1
 
2.4%
쏘패스트코리아(sfk 1
 
2.4%
msc 1
 
2.4%
Other values (30) 30
71.4%
2023-12-12T09:38:48.206239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 21
 
7.3%
) 21
 
7.3%
S 21
 
7.3%
L 18
 
6.2%
C 17
 
5.9%
M 14
 
4.9%
E 7
 
2.4%
H 7
 
2.4%
I 6
 
2.1%
N 6
 
2.1%
Other values (79) 150
52.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 135
46.9%
Other Letter 77
26.7%
Lowercase Letter 27
 
9.4%
Open Punctuation 21
 
7.3%
Close Punctuation 21
 
7.3%
Space Separator 4
 
1.4%
Dash Punctuation 3
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
운 5
 
6.5%
인 5
 
6.5%
라 5
 
6.5%
해 5
 
6.5%
선 4
 
5.2%
상 4
 
5.2%
스 3
 
3.9%
트 2
 
2.6%
영 2
 
2.6%
동 2
 
2.6%
Other values (39) 40
51.9%
Uppercase Letter
ValueCountFrequency (%)
S 21
15.6%
L 18
13.3%
C 17
12.6%
M 14
10.4%
E 7
 
5.2%
H 7
 
5.2%
I 6
 
4.4%
N 6
 
4.4%
A 5
 
3.7%
T 5
 
3.7%
Other values (12) 29
21.5%
Lowercase Letter
ValueCountFrequency (%)
o 4
14.8%
n 3
11.1%
g 3
11.1%
i 3
11.1%
p 3
11.1%
e 2
7.4%
a 2
7.4%
l 1
 
3.7%
y 1
 
3.7%
d 1
 
3.7%
Other values (4) 4
14.8%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
56.2%
Hangul 77
26.7%
Common 49
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
운 5
 
6.5%
인 5
 
6.5%
라 5
 
6.5%
해 5
 
6.5%
선 4
 
5.2%
상 4
 
5.2%
스 3
 
3.9%
트 2
 
2.6%
영 2
 
2.6%
동 2
 
2.6%
Other values (39) 40
51.9%
Latin
ValueCountFrequency (%)
S 21
 
13.0%
L 18
 
11.1%
C 17
 
10.5%
M 14
 
8.6%
E 7
 
4.3%
H 7
 
4.3%
I 6
 
3.7%
N 6
 
3.7%
A 5
 
3.1%
T 5
 
3.1%
Other values (26) 56
34.6%
Common
ValueCountFrequency (%)
( 21
42.9%
) 21
42.9%
4
 
8.2%
- 3
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211
73.3%
Hangul 77
 
26.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 21
 
10.0%
) 21
 
10.0%
S 21
 
10.0%
L 18
 
8.5%
C 17
 
8.1%
M 14
 
6.6%
E 7
 
3.3%
H 7
 
3.3%
I 6
 
2.8%
N 6
 
2.8%
Other values (30) 73
34.6%
Hangul
ValueCountFrequency (%)
운 5
 
6.5%
인 5
 
6.5%
라 5
 
6.5%
해 5
 
6.5%
선 4
 
5.2%
상 4
 
5.2%
스 3
 
3.9%
트 2
 
2.6%
영 2
 
2.6%
동 2
 
2.6%
Other values (39) 40
51.9%

코드
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T09:38:48.473136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0263158
Min length3

Characters and Unicode

Total characters115
Distinct characters22
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

Unique38 ?
Unique (%)100.0%

Sample

1st rowSML
2nd rowSKR
3rd rowHAS
4th rowHMM
5th rowKMD
ValueCountFrequency (%)
sml 1
 
2.6%
cnc 1
 
2.6%
esl 1
 
2.6%
tsl 1
 
2.6%
cma 1
 
2.6%
emc 1
 
2.6%
snt 1
 
2.6%
sfk 1
 
2.6%
msc 1
 
2.6%
oocl 1
 
2.6%
Other values (28) 28
73.7%
2023-12-12T09:38:48.859015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 21
18.3%
L 14
12.2%
C 14
12.2%
M 11
9.6%
O 6
 
5.2%
H 6
 
5.2%
E 5
 
4.3%
A 5
 
4.3%
P 4
 
3.5%
Y 4
 
3.5%
Other values (12) 25
21.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 115
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 21
18.3%
L 14
12.2%
C 14
12.2%
M 11
9.6%
O 6
 
5.2%
H 6
 
5.2%
E 5
 
4.3%
A 5
 
4.3%
P 4
 
3.5%
Y 4
 
3.5%
Other values (12) 25
21.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 115
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 21
18.3%
L 14
12.2%
C 14
12.2%
M 11
9.6%
O 6
 
5.2%
H 6
 
5.2%
E 5
 
4.3%
A 5
 
4.3%
P 4
 
3.5%
Y 4
 
3.5%
Other values (12) 25
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 21
18.3%
L 14
12.2%
C 14
12.2%
M 11
9.6%
O 6
 
5.2%
H 6
 
5.2%
E 5
 
4.3%
A 5
 
4.3%
P 4
 
3.5%
Y 4
 
3.5%
Other values (12) 25
21.7%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size436.0 B
외국적
25 
국적
13 

Length

Max length3
Median length3
Mean length2.6578947
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
외국적 25
65.8%
국적 13
34.2%

Length

2023-12-12T09:38:49.013201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:38:49.112378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
외국적 25
65.8%
국적 13
34.2%

주당항차수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1842105
Minimum0
Maximum12
Zeros13
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T09:38:49.212854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32.75
95-th percentile8.45
Maximum12
Range12
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation3.0031989
Coefficient of variation (CV)1.3749585
Kurtosis3.6206312
Mean2.1842105
Median Absolute Deviation (MAD)1
Skewness1.9741324
Sum83
Variance9.0192034
MonotonicityNot monotonic
2023-12-12T09:38:49.325690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 13
34.2%
1 9
23.7%
2 6
15.8%
3 3
 
7.9%
12 1
 
2.6%
6 1
 
2.6%
11 1
 
2.6%
7 1
 
2.6%
8 1
 
2.6%
4 1
 
2.6%
ValueCountFrequency (%)
0 13
34.2%
1 9
23.7%
2 6
15.8%
3 3
 
7.9%
4 1
 
2.6%
5 1
 
2.6%
6 1
 
2.6%
7 1
 
2.6%
8 1
 
2.6%
11 1
 
2.6%
ValueCountFrequency (%)
12 1
 
2.6%
11 1
 
2.6%
8 1
 
2.6%
7 1
 
2.6%
6 1
 
2.6%
5 1
 
2.6%
4 1
 
2.6%
3 3
 
7.9%
2 6
15.8%
1 9
23.7%

공동운항포함주당항차수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0263158
Minimum0
Maximum20
Zeros2
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T09:38:49.442328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.85
Q11
median2.5
Q34.75
95-th percentile15.45
Maximum20
Range20
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation4.6759121
Coefficient of variation (CV)1.1613376
Kurtosis4.7612671
Mean4.0263158
Median Absolute Deviation (MAD)1.5
Skewness2.1875017
Sum153
Variance21.864154
MonotonicityNot monotonic
2023-12-12T09:38:49.584660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 14
36.8%
4 7
18.4%
5 3
 
7.9%
2 3
 
7.9%
8 2
 
5.3%
0 2
 
5.3%
3 2
 
5.3%
18 1
 
2.6%
9 1
 
2.6%
20 1
 
2.6%
Other values (2) 2
 
5.3%
ValueCountFrequency (%)
0 2
 
5.3%
1 14
36.8%
2 3
 
7.9%
3 2
 
5.3%
4 7
18.4%
5 3
 
7.9%
6 1
 
2.6%
8 2
 
5.3%
9 1
 
2.6%
15 1
 
2.6%
ValueCountFrequency (%)
20 1
 
2.6%
18 1
 
2.6%
15 1
 
2.6%
9 1
 
2.6%
8 2
 
5.3%
6 1
 
2.6%
5 3
7.9%
4 7
18.4%
3 2
 
5.3%
2 3
7.9%

Interactions

2023-12-12T09:38:47.191551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:46.999259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:47.310827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:47.090370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:38:49.694212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선사별코드구분주당항차수공동운항포함주당항차수
선사별1.0001.0001.0001.0001.000
코드1.0001.0001.0001.0001.000
구분1.0001.0001.0000.6510.595
주당항차수1.0001.0000.6511.0000.844
공동운항포함주당항차수1.0001.0000.5950.8441.000
2023-12-12T09:38:49.789816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주당항차수공동운항포함주당항차수구분
주당항차수1.0000.7360.374
공동운항포함주당항차수0.7361.0000.511
구분0.3740.5111.000

Missing values

2023-12-12T09:38:47.457605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:38:47.555517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

선사별코드구분주당항차수공동운항포함주당항차수
0SM상선(SML)SML국적34
1장금상선(SKR)SKR국적1218
2흥아라인(HAS)HAS국적39
3HMMHMM국적68
4고려해운(KMD)KMD국적1120
5팬오션(POL)POL국적78
6남성해운(NSL)NSL국적815
7범주해운(PCL)PCL국적24
8동진상선(DJS)DJS국적14
9동영해운(DYS)DYS국적14
선사별코드구분주당항차수공동운항포함주당항차수
28Hapag-Lloyd(HLC)HLC외국적01
29OOCLOOCL외국적01
30ONEONE외국적05
31ZIM LINEZIM외국적01
32Cosco ShippingCOS외국적01
33PILPIL외국적01
34GFSGFS외국적01
35SITCHYS외국적00
36ESLESL외국적01
37볼타쉬핑(VSS)VSS외국적11