Overview

Dataset statistics

Number of variables5
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory47.5 B

Variable types

Numeric5

Dataset

Description1971년 부터 연도별 국내선 여객, 국제선 여객, 국내선 화물, 국제선 화물 항공수송실적 요약 통계 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15052301/fileData.do

Alerts

구분 is highly overall correlated with 국내선여객 and 3 other fieldsHigh correlation
국내선여객 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
국제선여객 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
국내선화물 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
국제선화물 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
구분 has unique valuesUnique
국내선여객 has unique valuesUnique
국제선여객 has unique valuesUnique
국내선화물 has unique valuesUnique
국제선화물 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:33:55.793845
Analysis finished2023-12-11 23:33:57.924374
Duration2.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.5
Minimum1971
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T08:33:58.003289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1971
5-th percentile1973.55
Q11983.75
median1996.5
Q32009.25
95-th percentile2019.45
Maximum2022
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.0075906623
Kurtosis-1.2
Mean1996.5
Median Absolute Deviation (MAD)13
Skewness0
Sum103818
Variance229.66667
MonotonicityStrictly increasing
2023-12-12T08:33:58.158885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1971 1
 
1.9%
1998 1
 
1.9%
2000 1
 
1.9%
2001 1
 
1.9%
2002 1
 
1.9%
2003 1
 
1.9%
2004 1
 
1.9%
2005 1
 
1.9%
2006 1
 
1.9%
2007 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1971 1
1.9%
1972 1
1.9%
1973 1
1.9%
1974 1
1.9%
1975 1
1.9%
1976 1
1.9%
1977 1
1.9%
1978 1
1.9%
1979 1
1.9%
1980 1
1.9%
ValueCountFrequency (%)
2022 1
1.9%
2021 1
1.9%
2020 1
1.9%
2019 1
1.9%
2018 1
1.9%
2017 1
1.9%
2016 1
1.9%
2015 1
1.9%
2014 1
1.9%
2013 1
1.9%

국내선여객
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15070637
Minimum795298
Maximum36328296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T08:33:58.316005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum795298
5-th percentile1049208.6
Q12742571.5
median17169340
Q321946843
95-th percentile32664876
Maximum36328296
Range35532998
Interquartile range (IQR)19204272

Descriptive statistics

Standard deviation10874949
Coefficient of variation (CV)0.72159849
Kurtosis-1.2019854
Mean15070637
Median Absolute Deviation (MAD)8343468.5
Skewness0.055145172
Sum7.8367314 × 108
Variance1.1826452 × 1014
MonotonicityNot monotonic
2023-12-12T08:33:58.805856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1105470 1
 
1.9%
19504413 1
 
1.9%
22514887 1
 
1.9%
21811334 1
 
1.9%
21248326 1
 
1.9%
21379524 1
 
1.9%
18892652 1
 
1.9%
17157595 1
 
1.9%
17181085 1
 
1.9%
16847870 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
795298 1
1.9%
905909 1
1.9%
991078 1
1.9%
1096770 1
1.9%
1105470 1
1.9%
1120922 1
1.9%
1269081 1
1.9%
1460781 1
1.9%
1480986 1
1.9%
1555220 1
1.9%
ValueCountFrequency (%)
36328296 1
1.9%
33146646 1
1.9%
32980968 1
1.9%
32406255 1
1.9%
31600610 1
1.9%
30912922 1
1.9%
27980135 1
1.9%
25638653 1
1.9%
25164038 1
1.9%
24647538 1
1.9%

국제선여객
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21885623
Minimum515244
Maximum90385640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T08:33:59.047316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum515244
5-th percentile1347903.2
Q13652278.5
median14172144
Q332909010
95-th percentile74780519
Maximum90385640
Range89870396
Interquartile range (IQR)29256732

Descriptive statistics

Standard deviation23470560
Coefficient of variation (CV)1.0724191
Kurtosis1.3934235
Mean21885623
Median Absolute Deviation (MAD)11073046
Skewness1.441603
Sum1.1380524 × 109
Variance5.508672 × 1014
MonotonicityNot monotonic
2023-12-12T08:33:59.192796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
515244 1
 
1.9%
14104367 1
 
1.9%
19452282 1
 
1.9%
20350504 1
 
1.9%
22716818 1
 
1.9%
21459288 1
 
1.9%
26930936 1
 
1.9%
29683846 1
 
1.9%
32707495 1
 
1.9%
36855905 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
515244 1
1.9%
784845 1
1.9%
1244067 1
1.9%
1432860 1
1.9%
1566116 1
1.9%
2014131 1
1.9%
2316391 1
1.9%
2708145 1
1.9%
2921574 1
1.9%
2988832 1
1.9%
ValueCountFrequency (%)
90385640 1
1.9%
85925288 1
1.9%
76955719 1
1.9%
73000810 1
1.9%
61434404 1
1.9%
56778759 1
1.9%
50986891 1
1.9%
47702644 1
1.9%
42648549 1
1.9%
40060948 1
1.9%

국내선화물
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208036.92
Minimum5430
Maximum434228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T08:33:59.369466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5430
5-th percentile6682.35
Q154137
median253462.5
Q3308660
95-th percentile426375.6
Maximum434228
Range428798
Interquartile range (IQR)254523

Descriptive statistics

Standard deviation144811.69
Coefficient of variation (CV)0.69608649
Kurtosis-1.3361885
Mean208036.92
Median Absolute Deviation (MAD)114504.5
Skewness-0.14317985
Sum10817920
Variance2.0970426 × 1010
MonotonicityNot monotonic
2023-12-12T08:33:59.541172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7284 1
 
1.9%
363548 1
 
1.9%
434228 1
 
1.9%
431033 1
 
1.9%
432701 1
 
1.9%
422565 1
 
1.9%
408984 1
 
1.9%
372386 1
 
1.9%
355249 1
 
1.9%
316397 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
5430 1
1.9%
5623 1
1.9%
5947 1
1.9%
7284 1
1.9%
7550 1
1.9%
7841 1
1.9%
8820 1
1.9%
11003 1
1.9%
12764 1
1.9%
13918 1
1.9%
ValueCountFrequency (%)
434228 1
1.9%
432701 1
1.9%
431033 1
1.9%
422565 1
1.9%
408984 1
1.9%
393275 1
1.9%
387319 1
1.9%
372386 1
1.9%
363548 1
1.9%
355249 1
1.9%

국제선화물
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1680035.2
Minimum28580
Maximum4168783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T08:33:59.716357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28580
5-th percentile64417.35
Q1292716.5
median1450861.5
Q33087735.8
95-th percentile3886727.6
Maximum4168783
Range4140203
Interquartile range (IQR)2795019.2

Descriptive statistics

Standard deviation1387506.3
Coefficient of variation (CV)0.82587929
Kurtosis-1.4671862
Mean1680035.2
Median Absolute Deviation (MAD)1271424
Skewness0.28789669
Sum87361829
Variance1.9251736 × 1012
MonotonicityNot monotonic
2023-12-12T08:33:59.874308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28580 1
 
1.9%
1470751 1
 
1.9%
1949352 1
 
1.9%
1863832 1
 
1.9%
2076806 1
 
1.9%
2208794 1
 
1.9%
2569133 1
 
1.9%
2616814 1
 
1.9%
2853534 1
 
1.9%
3137964 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
28580 1
1.9%
38035 1
1.9%
57885 1
1.9%
69762 1
1.9%
97752 1
1.9%
105248 1
1.9%
123612 1
1.9%
141999 1
1.9%
167465 1
1.9%
191410 1
1.9%
ValueCountFrequency (%)
4168783 1
1.9%
4031516 1
1.9%
4015987 1
1.9%
3780970 1
1.9%
3518772 1
1.9%
3420247 1
1.9%
3410743 1
1.9%
3326884 1
1.9%
3320436 1
1.9%
3246253 1
1.9%

Interactions

2023-12-12T08:33:57.381129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:55.936828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.306389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.681217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:57.031314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:57.451802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.014197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.385071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.749655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:57.096691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:57.530858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.097383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.469095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.828217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:57.172756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:57.598142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.169010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.539768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.893919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:57.239734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:57.675121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.235148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.609338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:56.965938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:33:57.307521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:33:59.974201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분국내선여객국제선여객국내선화물국제선화물
구분1.0000.8860.8120.9100.941
국내선여객0.8861.0000.7980.9200.927
국제선여객0.8120.7981.0000.6700.869
국내선화물0.9100.9200.6701.0000.905
국제선화물0.9410.9270.8690.9051.000
2023-12-12T08:34:00.115161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분국내선여객국제선여객국내선화물국제선화물
구분1.0000.9200.8840.6600.987
국내선여객0.9201.0000.8130.7460.910
국제선여객0.8840.8131.0000.7530.926
국내선화물0.6600.7460.7531.0000.686
국제선화물0.9870.9100.9260.6861.000

Missing values

2023-12-12T08:33:57.766919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:33:57.879813image/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

구분국내선여객국제선여객국내선화물국제선화물
019711105470515244728428580
119721120922784845784138035
2197312690811432860882057885
319749910781244067594769762
419759059091566116562397752
5197679529820141315430105248
61977109677023163917550123612
719781460781270814511003141999
819791811955298883213918167465
919801480986292157412764191410
구분국내선여객국제선여객국내선화물국제선화물
42201322353370509868912526863246253
43201424647538567787592831193410743
44201527980135614344042877813518772
45201630912922730008102928873780970
46201732406255769557192901254031516
47201831600610859252882731924168783
48201932980968903856402587304015987
49202025164038142399221817853070993
5020213314664632093642045853420247
51202236328296195000592293553320436