Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells2565
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory585.9 KiB
Average record size in memory60.0 B

Variable types

Numeric4
Categorical2

Dataset

Description- 국내 공항별로 월별, 도착출발별 운항 수, 이용자 수, 화물량 통계를 제공합니다. - 데이터 제공처: KOSIS 국가통계포털
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/785

Alerts

운항수(편) is highly overall correlated with 이용자수(명) and 1 other fieldsHigh correlation
이용자수(명) is highly overall correlated with 운항수(편) and 1 other fieldsHigh correlation
화물량(톤) is highly overall correlated with 운항수(편) and 1 other fieldsHigh correlation
운항수(편) has 855 (8.6%) missing valuesMissing
이용자수(명) has 855 (8.6%) missing valuesMissing
화물량(톤) has 855 (8.6%) missing valuesMissing

Reproduction

Analysis started2023-12-11 19:54:48.941128
Analysis finished2023-12-11 19:54:53.435123
Duration4.49 seconds
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%
Mean201379.26
Minimum200501
Maximum202306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:54:53.545816image/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 deviation533.40127
Coefficient of variation (CV)0.0026487399
Kurtosis-1.195483
Mean201379.26
Median Absolute Deviation (MAD)493
Skewness0.011294549
Sum2.0137926 × 109
Variance284516.92
MonotonicityNot monotonic
2023-12-12T04:54:53.785138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202107 50
 
0.5%
201712 50
 
0.5%
200705 50
 
0.5%
201907 49
 
0.5%
201909 49
 
0.5%
200512 49
 
0.5%
201006 49
 
0.5%
201312 49
 
0.5%
200501 48
 
0.5%
201610 48
 
0.5%
Other values (212) 9509
95.1%
ValueCountFrequency (%)
200501 48
0.5%
200502 45
0.4%
200503 46
0.5%
200504 46
0.5%
200505 44
0.4%
200506 47
0.5%
200507 43
0.4%
200508 46
0.5%
200509 45
0.4%
200510 43
0.4%
ValueCountFrequency (%)
202306 45
0.4%
202305 44
0.4%
202304 44
0.4%
202303 43
0.4%
202302 45
0.4%
202301 40
0.4%
202212 43
0.4%
202211 44
0.4%
202210 47
0.5%
202209 42
0.4%

공항
Categorical

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
양양
 
599
원주
 
598
여수
 
598
사천
 
596
무안
 
595
Other values (13)
7014 

Length

Max length4
Median length2
Mean length2.0132
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주
2nd row인천
3rd row대구
4th row제주
5th row사천

Common Values

ValueCountFrequency (%)
양양 599
 
6.0%
원주 598
 
6.0%
여수 598
 
6.0%
사천 596
 
6.0%
무안 595
 
5.9%
광주 594
 
5.9%
김포 589
 
5.9%
울산 589
 
5.9%
제주 588
 
5.9%
합계 587
 
5.9%
Other values (8) 4067
40.7%

Length

2023-12-12T04:54:53.988717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
양양 599
 
6.0%
원주 598
 
6.0%
여수 598
 
6.0%
사천 596
 
6.0%
무안 595
 
5.9%
광주 594
 
5.9%
김포 589
 
5.9%
울산 589
 
5.9%
제주 588
 
5.9%
합계 587
 
5.9%
Other values (8) 4067
40.7%

도착출발구분
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
도착
3353 
계
3344 
출발
3303 

Length

Max length2
Median length2
Mean length1.6656
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도착 3353
33.5%
계 3344
33.4%
출발 3303
33.0%

Length

2023-12-12T04:54:54.168292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:54:54.309493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도착 3353
33.5%
계 3344
33.4%
출발 3303
33.0%

운항수(편)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3813
Distinct (%)41.7%
Missing855
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean4580.0108
Minimum0
Maximum81444
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:54:54.480731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q1106
median447
Q34632
95-th percentile25152.2
Maximum81444
Range81444
Interquartile range (IQR)4526

Descriptive statistics

Standard deviation10028.781
Coefficient of variation (CV)2.189685
Kurtosis15.967215
Mean4580.0108
Median Absolute Deviation (MAD)391
Skewness3.6627884
Sum41884199
Variance1.0057645 × 108
MonotonicityNot monotonic
2023-12-12T04:54:54.725150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 155
 
1.6%
62 121
 
1.2%
30 114
 
1.1%
29 79
 
0.8%
31 74
 
0.7%
58 72
 
0.7%
28 56
 
0.6%
124 55
 
0.5%
120 54
 
0.5%
56 48
 
0.5%
Other values (3803) 8317
83.2%
(Missing) 855
 
8.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 10
 
0.1%
2 22
0.2%
3 5
 
0.1%
4 19
0.2%
5 10
 
0.1%
6 9
 
0.1%
7 13
 
0.1%
8 33
0.3%
9 23
0.2%
ValueCountFrequency (%)
81444 1
< 0.1%
78976 1
< 0.1%
77117 1
< 0.1%
76728 1
< 0.1%
76649 1
< 0.1%
76610 1
< 0.1%
76079 1
< 0.1%
75828 1
< 0.1%
75359 1
< 0.1%
75292 1
< 0.1%

이용자수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8809
Distinct (%)96.3%
Missing855
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean693078.16
Minimum0
Maximum14281476
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:54:54.949183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2493.2
Q110656
median52979
Q3629454
95-th percentile3662122
Maximum14281476
Range14281476
Interquartile range (IQR)618798

Descriptive statistics

Standard deviation1577709.8
Coefficient of variation (CV)2.2763808
Kurtosis18.992174
Mean693078.16
Median Absolute Deviation (MAD)48927
Skewness3.9040696
Sum6.3381997 × 109
Variance2.4891682 × 1012
MonotonicityNot monotonic
2023-12-12T04:54:55.163626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
932 4
 
< 0.1%
2627 3
 
< 0.1%
5664 3
 
< 0.1%
7469 3
 
< 0.1%
3524 3
 
< 0.1%
6740 3
 
< 0.1%
3483 3
 
< 0.1%
3819 3
 
< 0.1%
3293 3
 
< 0.1%
12762 3
 
< 0.1%
Other values (8799) 9114
91.1%
(Missing) 855
 
8.6%
ValueCountFrequency (%)
0 2
< 0.1%
39 1
< 0.1%
51 1
< 0.1%
54 1
< 0.1%
63 1
< 0.1%
66 1
< 0.1%
78 1
< 0.1%
86 1
< 0.1%
94 1
< 0.1%
105 1
< 0.1%
ValueCountFrequency (%)
14281476 1
< 0.1%
13536971 1
< 0.1%
13463725 1
< 0.1%
13450302 1
< 0.1%
13219221 1
< 0.1%
13034574 1
< 0.1%
13018518 1
< 0.1%
12899524 1
< 0.1%
12880754 1
< 0.1%
12790595 1
< 0.1%

화물량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8287
Distinct (%)90.6%
Missing855
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean27762.354
Minimum0
Maximum414856.51
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:54:55.390018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.42
Q162.51
median398.44
Q38716
95-th percentile184726.37
Maximum414856.51
Range414856.51
Interquartile range (IQR)8653.49

Descriptive statistics

Standard deviation70934.899
Coefficient of variation (CV)2.5550751
Kurtosis8.2243579
Mean27762.354
Median Absolute Deviation (MAD)380
Skewness2.9442981
Sum2.5388673 × 108
Variance5.0317598 × 109
MonotonicityNot monotonic
2023-12-12T04:54:55.579127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.0 10
 
0.1%
39.0 9
 
0.1%
100.0 9
 
0.1%
47.0 9
 
0.1%
41.0 8
 
0.1%
59.0 8
 
0.1%
2.0 8
 
0.1%
15.0 7
 
0.1%
46.0 7
 
0.1%
0.0 7
 
0.1%
Other values (8277) 9063
90.6%
(Missing) 855
 
8.6%
ValueCountFrequency (%)
0.0 7
0.1%
0.27 1
 
< 0.1%
0.3 2
 
< 0.1%
0.4 2
 
< 0.1%
0.41 2
 
< 0.1%
0.47 1
 
< 0.1%
0.49 1
 
< 0.1%
0.5 3
< 0.1%
0.55 1
 
< 0.1%
0.6 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

2023-12-12T04:54:52.368542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:50.055590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:50.869906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:51.612735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:52.558626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:50.252395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:51.076934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:51.799985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:52.728993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:50.458249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:51.248701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:51.998153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:52.902510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:50.675863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:51.448600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:54:52.194631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:54:55.697947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일(월)공항도착출발구분운항수(편)이용자수(명)화물량(톤)
기준일(월)1.0000.1560.0000.3210.2780.168
공항0.1561.0000.0000.7110.6720.796
도착출발구분0.0000.0001.0000.2970.2420.494
운항수(편)0.3210.7110.2971.0000.9740.817
이용자수(명)0.2780.6720.2420.9741.0000.796
화물량(톤)0.1680.7960.4940.8170.7961.000
2023-12-12T04:54:55.883864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공항도착출발구분
공항1.0000.000
도착출발구분0.0001.000
2023-12-12T04:54:56.062603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일(월)운항수(편)이용자수(명)화물량(톤)공항도착출발구분
기준일(월)1.0000.0540.0780.0180.0600.000
운항수(편)0.0541.0000.9930.9720.3670.186
이용자수(명)0.0780.9931.0000.9780.3320.149
화물량(톤)0.0180.9720.9781.0000.3970.250
공항0.0600.3670.3320.3971.0000.000
도착출발구분0.0000.1860.1490.2500.0001.000

Missing values

2023-12-12T04:54:53.052787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:54:53.202198image/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.
2023-12-12T04:54:53.345984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준일(월)공항도착출발구분운항수(편)이용자수(명)화물량(톤)
3162201003광주계9061141301353.25
6973201605인천도착140132242829136236.39
6491201508대구출발6481020751053.01
1569200707제주계8420101254121428.51
6704201512사천출발77594824.14
6544201509목포도착<NA><NA><NA>
7250201611김포출발5889100364911372.76
7773201709사천계1561531362.52
2812200908김포도착527578589910475.75
4133201110광주출발45664485754.57
기준일(월)공항도착출발구분운항수(편)이용자수(명)화물량(톤)
6463201507인천도착115441781957132717.98
1939200803광주도착49358382871.32
10512202203김포계11430169050711816.0
741200603여수계57851985171.11
5279201308양양출발27395053.02
3788201103대구출발35144679767.34
7629201706울산계41642201197.78
185200504울산출발4955425355.92
5752201405제주도착65179513719824.95
1540200707김해도착25232993704617.71