Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory400.4 KiB
Average record size in memory41.0 B

Variable types

Numeric1
Categorical2
Text1

Dataset

Description- 제주특별차지도에 위치한 항구별로 화물별 수송량에 대한 정보를 제공합니다. - 데이터 제공처: KOSIS 국가통계포털
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/771

Reproduction

Analysis started2023-12-11 20:01:46.312253
Analysis finished2023-12-11 20:01:46.926225
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일(월)
Real number (ℝ)

Distinct132
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201606.44
Minimum201101
Maximum202112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:01:47.026106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201101
5-th percentile201107
Q1201309
median201606
Q3201904
95-th percentile202106
Maximum202112
Range1011
Interquartile range (IQR)595

Descriptive statistics

Standard deviation316.15531
Coefficient of variation (CV)0.0015681806
Kurtosis-1.220503
Mean201606.44
Median Absolute Deviation (MAD)297
Skewness0.0029446369
Sum2.0160644 × 109
Variance99954.183
MonotonicityNot monotonic
2023-12-12T05:01:47.179184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202010 82
 
0.8%
201911 81
 
0.8%
201407 81
 
0.8%
201611 80
 
0.8%
201305 80
 
0.8%
201907 80
 
0.8%
201802 80
 
0.8%
201612 80
 
0.8%
201909 79
 
0.8%
201509 79
 
0.8%
Other values (122) 9198
92.0%
ValueCountFrequency (%)
201101 77
0.8%
201102 74
0.7%
201103 75
0.8%
201104 75
0.8%
201105 73
0.7%
201106 78
0.8%
201107 75
0.8%
201108 70
0.7%
201109 76
0.8%
201110 73
0.7%
ValueCountFrequency (%)
202112 75
0.8%
202111 76
0.8%
202110 79
0.8%
202109 73
0.7%
202108 78
0.8%
202107 72
0.7%
202106 75
0.8%
202105 77
0.8%
202104 74
0.7%
202103 79
0.8%

항구
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전체
1446 
애월항
1439 
제주항
1430 
화순항
1429 
성산포항
1420 
Other values (2)
2836 

Length

Max length4
Median length3
Mean length3.1393
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서귀포항
2nd row화순항
3rd row서귀포항
4th row한림항
5th row서귀포항

Common Values

ValueCountFrequency (%)
전체 1446
14.5%
애월항 1439
14.4%
제주항 1430
14.3%
화순항 1429
14.3%
성산포항 1420
14.2%
서귀포항 1419
14.2%
한림항 1417
14.2%

Length

2023-12-12T05:01:47.323504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:01:47.458364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 1446
14.5%
애월항 1439
14.4%
제주항 1430
14.3%
화순항 1429
14.3%
성산포항 1420
14.2%
서귀포항 1419
14.2%
한림항 1417
14.2%

화물구분
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
유류
845 
선어
842 
시멘트
839 
비료
839 
목재
836 
Other values (7)
5799 

Length

Max length3
Median length2
Mean length2.2504
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row모래
2nd row모래
3rd row선어
4th row무연탄
5th row목재

Common Values

ValueCountFrequency (%)
유류 845
8.5%
선어 842
8.4%
시멘트 839
8.4%
비료 839
8.4%
목재 836
8.4%
무연탄 834
8.3%
모래 833
8.3%
철재 833
8.3%
유연탄 831
8.3%
채소 828
8.3%
Other values (2) 1640
16.4%

Length

2023-12-12T05:01:47.595671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
유류 845
8.5%
선어 842
8.4%
시멘트 839
8.4%
비료 839
8.4%
목재 836
8.4%
무연탄 834
8.3%
모래 833
8.3%
철재 833
8.3%
유연탄 831
8.3%
채소 828
8.3%
Other values (2) 1640
16.4%

화물량(톤)
Text

Distinct3434
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:01:47.939293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length1
Mean length2.5678
Min length1

Characters and Unicode

Total characters25678
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2950 ?
Unique (%)29.5%

Sample

1st row7140
2nd row13650
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
5665
56.7%
0 40
 
0.4%
500 21
 
0.2%
50 20
 
0.2%
3000 16
 
0.2%
480 14
 
0.1%
1500 13
 
0.1%
6200 13
 
0.1%
1000 13
 
0.1%
4000 13
 
0.1%
Other values (3423) 4171
41.7%
2023-12-12T05:01:48.494186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 5665
22.1%
0 3449
13.4%
1 2661
10.4%
2 2267
8.8%
5 1894
 
7.4%
3 1804
 
7.0%
6 1632
 
6.4%
4 1620
 
6.3%
8 1619
 
6.3%
7 1534
 
6.0%
Other values (3) 1533
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19933
77.6%
Dash Punctuation 5665
 
22.1%
Other Punctuation 55
 
0.2%
Space Separator 25
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3449
17.3%
1 2661
13.3%
2 2267
11.4%
5 1894
9.5%
3 1804
9.1%
6 1632
8.2%
4 1620
8.1%
8 1619
8.1%
7 1534
7.7%
9 1453
7.3%
Dash Punctuation
ValueCountFrequency (%)
- 5665
100.0%
Other Punctuation
ValueCountFrequency (%)
. 55
100.0%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 5665
22.1%
0 3449
13.4%
1 2661
10.4%
2 2267
8.8%
5 1894
 
7.4%
3 1804
 
7.0%
6 1632
 
6.4%
4 1620
 
6.3%
8 1619
 
6.3%
7 1534
 
6.0%
Other values (3) 1533
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 5665
22.1%
0 3449
13.4%
1 2661
10.4%
2 2267
8.8%
5 1894
 
7.4%
3 1804
 
7.0%
6 1632
 
6.4%
4 1620
 
6.3%
8 1619
 
6.3%
7 1534
 
6.0%
Other values (3) 1533
 
6.0%

Interactions

2023-12-12T05:01:46.604879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:01:48.605527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일(월)항구화물구분
기준일(월)1.0000.0000.000
항구0.0001.0000.000
화물구분0.0000.0001.000
2023-12-12T05:01:48.700234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
항구화물구분
항구1.0000.000
화물구분0.0001.000
2023-12-12T05:01:48.790895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일(월)항구화물구분
기준일(월)1.0000.0000.000
항구0.0001.0000.000
화물구분0.0000.0001.000

Missing values

2023-12-12T05:01:46.759725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:01:46.868275image/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

기준일(월)항구화물구분화물량(톤)
2965202008서귀포항모래7140
10081201501화순항모래13650
2021201401서귀포항선어-
8163201209한림항무연탄-
1814201208서귀포항목재-
150201201전체시멘트78522
5260201407애월항비료-
5521201605애월항모래65000
10456201708화순항비료-
3568201310성산포항비료-
기준일(월)항구화물구분화물량(톤)
3049202103서귀포항모래4000
9217202001한림항모래4000
2115201409서귀포항무연탄-
8666201603한림항목재-
1452202102전체기타1149337
5764201801애월항비료-
118201110전체채소37298
5727201710애월항무연탄-
5687201706애월항철재-
10529201802화순항선어-