Overview

Dataset statistics

Number of variables3
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory27.7 B

Variable types

Text1
Boolean1
Numeric1

Dataset

Description광양항을 이용하는 컨테이너선의 기항 현황(국가별 기항 현황)데이터 입니다. 국가, 기항여부, 기항항만수로 구성되어 있습니다. 데이터 갱신주기는 연간입니다.
Author여수광양항만공사
URLhttps://www.data.go.kr/data/15060305/fileData.do

Alerts

기항국가 has unique valuesUnique
항만수 has 17 (34.7%) zerosZeros

Reproduction

Analysis started2023-12-12 15:45:23.778167
Analysis finished2023-12-12 15:45:24.127014
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기항국가
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-13T00:45:24.295662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length3.122449
Min length2

Characters and Unicode

Total characters153
Distinct characters85
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row말레이시아
2nd row베트남
3rd row싱가폴
4th row인도네시아
5th row캄보디아
ValueCountFrequency (%)
말레이시아 1
 
2.0%
멕시코 1
 
2.0%
콜롬비아 1
 
2.0%
파나마 1
 
2.0%
페루 1
 
2.0%
뉴질랜드 1
 
2.0%
uae 1
 
2.0%
바레인 1
 
2.0%
사우디아라비아 1
 
2.0%
스리랑카 1
 
2.0%
Other values (39) 39
79.6%
2023-12-13T00:45:24.700761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 11
 
7.2%
스 6
 
3.9%
이 6
 
3.9%
나 6
 
3.9%
리 6
 
3.9%
국 5
 
3.3%
시 4
 
2.6%
인 4
 
2.6%
드 3
 
2.0%
마 3
 
2.0%
Other values (75) 99
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150
98.0%
Uppercase Letter 3
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 11
 
7.3%
스 6
 
4.0%
이 6
 
4.0%
나 6
 
4.0%
리 6
 
4.0%
국 5
 
3.3%
시 4
 
2.7%
인 4
 
2.7%
드 3
 
2.0%
마 3
 
2.0%
Other values (72) 96
64.0%
Uppercase Letter
ValueCountFrequency (%)
E 1
33.3%
A 1
33.3%
U 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150
98.0%
Latin 3
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 11
 
7.3%
스 6
 
4.0%
이 6
 
4.0%
나 6
 
4.0%
리 6
 
4.0%
국 5
 
3.3%
시 4
 
2.7%
인 4
 
2.7%
드 3
 
2.0%
마 3
 
2.0%
Other values (72) 96
64.0%
Latin
ValueCountFrequency (%)
E 1
33.3%
A 1
33.3%
U 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150
98.0%
ASCII 3
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 11
 
7.3%
스 6
 
4.0%
이 6
 
4.0%
나 6
 
4.0%
리 6
 
4.0%
국 5
 
3.3%
시 4
 
2.7%
인 4
 
2.7%
드 3
 
2.0%
마 3
 
2.0%
Other values (72) 96
64.0%
ASCII
ValueCountFrequency (%)
E 1
33.3%
A 1
33.3%
U 1
33.3%

기항여부
Boolean

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size181.0 B
True
32 
False
17 
ValueCountFrequency (%)
True 32
65.3%
False 17
34.7%
2023-12-13T00:45:24.835266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

항만수
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2653061
Minimum0
Maximum24
Zeros17
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T00:45:24.931895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8.2
Maximum24
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.6313043
Coefficient of variation (CV)2.0444496
Kurtosis14.739879
Mean2.2653061
Median Absolute Deviation (MAD)1
Skewness3.7329131
Sum111
Variance21.44898
MonotonicityNot monotonic
2023-12-13T00:45:25.067778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 18
36.7%
0 17
34.7%
2 4
 
8.2%
5 2
 
4.1%
3 2
 
4.1%
4 2
 
4.1%
9 1
 
2.0%
24 1
 
2.0%
21 1
 
2.0%
7 1
 
2.0%
ValueCountFrequency (%)
0 17
34.7%
1 18
36.7%
2 4
 
8.2%
3 2
 
4.1%
4 2
 
4.1%
5 2
 
4.1%
7 1
 
2.0%
9 1
 
2.0%
21 1
 
2.0%
24 1
 
2.0%
ValueCountFrequency (%)
24 1
 
2.0%
21 1
 
2.0%
9 1
 
2.0%
7 1
 
2.0%
5 2
 
4.1%
4 2
 
4.1%
3 2
 
4.1%
2 4
 
8.2%
1 18
36.7%
0 17
34.7%

Interactions

2023-12-13T00:45:23.893425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:45:25.167218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기항국가기항여부항만수
기항국가1.0001.0001.000
기항여부1.0001.0000.271
항만수1.0000.2711.000
2023-12-13T00:45:25.293350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
항만수기항여부
항만수1.0000.181
기항여부0.1811.000

Missing values

2023-12-13T00:45:24.007691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:45:24.097748image/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

기항국가기항여부항만수
0말레이시아Y5
1베트남Y5
2싱가폴Y1
3인도네시아Y2
4캄보디아N0
5태국Y2
6필리핀Y3
7브루나이Y1
8대만Y4
9대한민국Y9
기항국가기항여부항만수
39오만N0
40토고Y1
41남아공Y1
42모리셔스Y1
43미얀마Y1
44나이지리아Y1
45나미비아Y1
46베닌Y1
47이탈리아Y1
48가나Y1