Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells854
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

Numeric2
Categorical2

Dataset

Description- 국내 공항별로 월별, 도착출발별 화물량 통계를 제공합니다. - 데이터 제공처: KOSIS 국가통계포털
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/788

Alerts

화물량(톤) has 854 (8.5%) missing valuesMissing

Reproduction

Analysis started2023-12-11 20:09:30.789408
Analysis finished2023-12-11 20:09:31.830239
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일(월)
Real number (ℝ)

Distinct222
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201378.03
Minimum200501
Maximum202306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:09:31.926119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200501
5-th percentile200512
Q1200908
median201403
Q3201810
95-th percentile202207
Maximum202306
Range1805
Interquartile range (IQR)902

Descriptive statistics

Standard deviation533.36953
Coefficient of variation (CV)0.0026485985
Kurtosis-1.1952279
Mean201378.03
Median Absolute Deviation (MAD)493
Skewness0.017153136
Sum2.0137803 × 109
Variance284483.06
MonotonicityNot monotonic
2023-12-12T05:09:32.115165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200503 51
 
0.5%
200704 50
 
0.5%
200605 50
 
0.5%
201609 50
 
0.5%
202108 49
 
0.5%
201304 49
 
0.5%
201010 49
 
0.5%
200703 49
 
0.5%
201608 49
 
0.5%
200809 49
 
0.5%
Other values (212) 9505
95.0%
ValueCountFrequency (%)
200501 43
0.4%
200502 46
0.5%
200503 51
0.5%
200504 42
0.4%
200505 46
0.5%
200506 44
0.4%
200507 43
0.4%
200508 41
0.4%
200509 44
0.4%
200510 44
0.4%
ValueCountFrequency (%)
202306 46
0.5%
202305 44
0.4%
202304 41
0.4%
202303 47
0.5%
202302 46
0.5%
202301 41
0.4%
202212 46
0.5%
202211 44
0.4%
202210 44
0.4%
202209 42
0.4%

공항
Categorical

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여수
 
606
인천
 
604
제주
 
597
청주
 
595
합계
 
595
Other values (13)
7003 

Length

Max length4
Median length2
Mean length2.0134
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천
2nd row원주
3rd row청주
4th row광주
5th row인천

Common Values

ValueCountFrequency (%)
여수 606
 
6.1%
인천 604
 
6.0%
제주 597
 
6.0%
청주 595
 
5.9%
합계 595
 
5.9%
대구 594
 
5.9%
김포 590
 
5.9%
군산 590
 
5.9%
김해 590
 
5.9%
원주 590
 
5.9%
Other values (8) 4049
40.5%

Length

2023-12-12T05:09:32.300592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여수 606
 
6.1%
인천 604
 
6.0%
제주 597
 
6.0%
청주 595
 
5.9%
합계 595
 
5.9%
대구 594
 
5.9%
김해 590
 
5.9%
원주 590
 
5.9%
군산 590
 
5.9%
김포 590
 
5.9%
Other values (8) 4049
40.5%

도착출발구분
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
출발
3346 
계
3340 
도착
3314 

Length

Max length2
Median length2
Mean length1.666
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
출발 3346
33.5%
계 3340
33.4%
도착 3314
33.1%

Length

2023-12-12T05:09:32.454769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:09:32.563806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
출발 3346
33.5%
계 3340
33.4%
도착 3314
33.1%

화물량(톤)
Real number (ℝ)

MISSING 

Distinct8321
Distinct (%)91.0%
Missing854
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean28435.714
Minimum0
Maximum414856.51
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:09:32.709321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.9625
Q164.02
median447.635
Q39041.02
95-th percentile187991.93
Maximum414856.51
Range414856.51
Interquartile range (IQR)8977

Descriptive statistics

Standard deviation71834.187
Coefficient of variation (CV)2.526196
Kurtosis7.9628538
Mean28435.714
Median Absolute Deviation (MAD)425.655
Skewness2.9040914
Sum2.6007304 × 108
Variance5.1601505 × 109
MonotonicityNot monotonic
2023-12-12T05:09:33.161518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.0 10
 
0.1%
100.0 9
 
0.1%
41.0 9
 
0.1%
43.0 9
 
0.1%
59.0 8
 
0.1%
46.0 8
 
0.1%
48.0 8
 
0.1%
70.0 7
 
0.1%
17.0 7
 
0.1%
25.0 7
 
0.1%
Other values (8311) 9064
90.6%
(Missing) 854
 
8.5%
ValueCountFrequency (%)
0.0 7
0.1%
0.2 1
 
< 0.1%
0.27 1
 
< 0.1%
0.3 2
 
< 0.1%
0.4 2
 
< 0.1%
0.41 1
 
< 0.1%
0.47 1
 
< 0.1%
0.49 1
 
< 0.1%
0.5 1
 
< 0.1%
0.55 1
 
< 0.1%
ValueCountFrequency (%)
414856.51 1
< 0.1%
412385.84 1
< 0.1%
404501.42 1
< 0.1%
403682.16 1
< 0.1%
403435.88 1
< 0.1%
399976.73 1
< 0.1%
399353.57 1
< 0.1%
398809.03 1
< 0.1%
397610.71 1
< 0.1%
397271.71 1
< 0.1%

Interactions

2023-12-12T05:09:31.353208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:09:31.132932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:09:31.482306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:09:31.258288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:09:33.258361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일(월)공항도착출발구분화물량(톤)
기준일(월)1.0000.1570.0000.163
공항0.1571.0000.0000.796
도착출발구분0.0000.0001.0000.499
화물량(톤)0.1630.7960.4991.000
2023-12-12T05:09:33.392738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도착출발구분공항
도착출발구분1.0000.000
공항0.0001.000
2023-12-12T05:09:33.497934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일(월)화물량(톤)공항도착출발구분
기준일(월)1.0000.0170.0610.000
화물량(톤)0.0171.0000.3960.254
공항0.0610.3961.0000.000
도착출발구분0.0000.2540.0001.000

Missing values

2023-12-12T05:09:31.655108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:09:31.774339image/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)공항도착출발구분화물량(톤)
4483201204인천도착117818.89
604200512원주도착15.3
8479201811청주도착735.06
943200607광주도착672.66
6319201504인천도착138134.76
8754201904포항계30.91
1899200802청주계1233.16
2489200901군산출발47.92
3611201011군산출발67.29
6023201411김포출발11827.49
기준일(월)공항도착출발구분화물량(톤)
2724200906양양계<NA>
4007201107울산출발133.94
5085201304사천계54.16
1736200711합계출발174465.78
111200503제주계31961.34
7989201801포항계24.01
11236202305여수출발108.0
9164201912포항출발16.67
5658201403목포계<NA>
4954201302김해도착4943.93