Overview

Dataset statistics

Number of variables8
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory71.9 B

Variable types

Categorical4
Numeric4

Dataset

Description공항별 여객실적 통계 서비스입니다. 공항별로 국내선과 국제선으로 나누어 여객성인 출발/도착 수, 여객유아 출발/도착 수를 표시합니다.
URLhttps://www.data.go.kr/data/15002610/fileData.do

Alerts

연도 has constant value ""Constant
여객성인도착 is highly overall correlated with 여객성인출발 and 2 other fieldsHigh correlation
여객성인출발 is highly overall correlated with 여객성인도착 and 2 other fieldsHigh correlation
여객유아도착 is highly overall correlated with 여객성인도착 and 2 other fieldsHigh correlation
여객유아출발 is highly overall correlated with 여객성인도착 and 2 other fieldsHigh correlation
여객성인도착 has 6 (13.0%) zerosZeros
여객성인출발 has 7 (15.2%) zerosZeros
여객유아도착 has 6 (13.0%) zerosZeros
여객유아출발 has 8 (17.4%) zerosZeros

Reproduction

Analysis started2023-12-12 17:37:21.018713
Analysis finished2023-12-12 17:37:23.357117
Duration2.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 46
100.0%

Length

2023-12-13T02:37:23.415535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:37:23.516525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 46
100.0%

공항
Categorical

Distinct15
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size500.0 B
인천 INCHEON
4 
김포 GIMPO
4 
김해 GIMHAE
4 
제주 JEJU
4 
대구 DAEGU
4 
Other values (10)
26 

Length

Max length21
Median length10
Mean length9.3913043
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천 INCHEON
2nd row인천 INCHEON
3rd row김포 GIMPO
4th row김포 GIMPO
5th row김해 GIMHAE

Common Values

ValueCountFrequency (%)
인천 INCHEON 4
 
8.7%
김포 GIMPO 4
 
8.7%
김해 GIMHAE 4
 
8.7%
제주 JEJU 4
 
8.7%
대구 DAEGU 4
 
8.7%
청주 CHEONGJU 4
 
8.7%
무안 MUAN 4
 
8.7%
양양 YANGYANG 4
 
8.7%
광주 GWANGJU 2
 
4.3%
여수 YEOSU 2
 
4.3%
Other values (5) 10
21.7%

Length

2023-12-13T02:37:23.643046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천 4
 
4.2%
daegu 4
 
4.2%
incheon 4
 
4.2%
yangyang 4
 
4.2%
양양 4
 
4.2%
muan 4
 
4.2%
무안 4
 
4.2%
청주 4
 
4.2%
cheongju 4
 
4.2%
대구 4
 
4.2%
Other values (22) 56
58.3%

노선
Categorical

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
국내선
30 
국제선
16 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국내선
2nd row국제선
3rd row국내선
4th row국제선
5th row국내선

Common Values

ValueCountFrequency (%)
국내선 30
65.2%
국제선 16
34.8%

Length

2023-12-13T02:37:23.795135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:37:23.889946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내선 30
65.2%
국제선 16
34.8%

정기_부정기여부
Categorical

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
정기
23 
부정기
23 

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 23
50.0%
부정기 23
50.0%

Length

2023-12-13T02:37:23.989454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:37:24.094773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 23
50.0%
부정기 23
50.0%

여객성인도착
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean800232.57
Minimum0
Maximum14237886
Zeros6
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T02:37:24.257348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16925.75
median54131.5
Q3232400.5
95-th percentile4749961.5
Maximum14237886
Range14237886
Interquartile range (IQR)225474.75

Descriptive statistics

Standard deviation2482088.2
Coefficient of variation (CV)3.1017086
Kurtosis20.706291
Mean800232.57
Median Absolute Deviation (MAD)54131.5
Skewness4.3900522
Sum36810698
Variance6.1607619 × 1012
MonotonicityNot monotonic
2023-12-13T02:37:24.449964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 6
 
13.0%
83964 1
 
2.2%
9764 1
 
2.2%
482471 1
 
2.2%
216847 1
 
2.2%
142078 1
 
2.2%
122413 1
 
2.2%
98399 1
 
2.2%
177233 1
 
2.2%
25051 1
 
2.2%
Other values (31) 31
67.4%
ValueCountFrequency (%)
0 6
13.0%
134 1
 
2.2%
322 1
 
2.2%
338 1
 
2.2%
851 1
 
2.2%
932 1
 
2.2%
6359 1
 
2.2%
8626 1
 
2.2%
9635 1
 
2.2%
9764 1
 
2.2%
ValueCountFrequency (%)
14237886 1
2.2%
7919075 1
2.2%
5665721 1
2.2%
2002683 1
2.2%
1621229 1
2.2%
880944 1
2.2%
662969 1
2.2%
646731 1
2.2%
584302 1
2.2%
482471 1
2.2%

여객성인출발
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean800857.52
Minimum0
Maximum14297295
Zeros7
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T02:37:24.598559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12156.25
median55500
Q3224264.25
95-th percentile4758836.2
Maximum14297295
Range14297295
Interquartile range (IQR)222108

Descriptive statistics

Standard deviation2486109
Coefficient of variation (CV)3.1043088
Kurtosis20.880459
Mean800857.52
Median Absolute Deviation (MAD)55500
Skewness4.4044715
Sum36839446
Variance6.1807381 × 1012
MonotonicityNot monotonic
2023-12-13T02:37:24.753824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 7
 
15.2%
84258 1
 
2.2%
8563 1
 
2.2%
456406 1
 
2.2%
191637 1
 
2.2%
143636 1
 
2.2%
120899 1
 
2.2%
96793 1
 
2.2%
217731 1
 
2.2%
24517 1
 
2.2%
Other values (30) 30
65.2%
ValueCountFrequency (%)
0 7
15.2%
40 1
 
2.2%
350 1
 
2.2%
710 1
 
2.2%
767 1
 
2.2%
810 1
 
2.2%
6195 1
 
2.2%
8289 1
 
2.2%
8563 1
 
2.2%
11046 1
 
2.2%
ValueCountFrequency (%)
14297295 1
2.2%
7863005 1
2.2%
5667462 1
2.2%
2032959 1
2.2%
1620156 1
2.2%
885476 1
2.2%
664498 1
2.2%
647180 1
2.2%
589181 1
2.2%
456406 1
2.2%

여객유아도착
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5317.8261
Minimum0
Maximum75091
Zeros6
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T02:37:24.889914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.25
median414.5
Q31511.75
95-th percentile39510.5
Maximum75091
Range75091
Interquartile range (IQR)1500.5

Descriptive statistics

Standard deviation15328.22
Coefficient of variation (CV)2.8824223
Kurtosis13.042878
Mean5317.8261
Median Absolute Deviation (MAD)414.5
Skewness3.6653493
Sum244620
Variance2.3495434 × 108
MonotonicityNot monotonic
2023-12-13T02:37:25.049907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 6
 
13.0%
9 2
 
4.3%
496 1
 
2.2%
90 1
 
2.2%
61 1
 
2.2%
1918 1
 
2.2%
1537 1
 
2.2%
523 1
 
2.2%
1015 1
 
2.2%
353 1
 
2.2%
Other values (30) 30
65.2%
ValueCountFrequency (%)
0 6
13.0%
1 1
 
2.2%
2 1
 
2.2%
6 1
 
2.2%
9 2
 
4.3%
11 1
 
2.2%
12 1
 
2.2%
27 1
 
2.2%
61 1
 
2.2%
90 1
 
2.2%
ValueCountFrequency (%)
75091 1
2.2%
58985 1
2.2%
47971 1
2.2%
14129 1
2.2%
9968 1
2.2%
8310 1
2.2%
5723 1
2.2%
5236 1
2.2%
2617 1
2.2%
1918 1
2.2%

여객유아출발
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5498.2609
Minimum0
Maximum73445
Zeros8
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T02:37:25.203142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.5
median416.5
Q31669.5
95-th percentile40214
Maximum73445
Range73445
Interquartile range (IQR)1660

Descriptive statistics

Standard deviation15877.616
Coefficient of variation (CV)2.8877525
Kurtosis12.419416
Mean5498.2609
Median Absolute Deviation (MAD)416.5
Skewness3.6149908
Sum252920
Variance2.520987 × 108
MonotonicityNot monotonic
2023-12-13T02:37:25.332357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 8
 
17.4%
116 2
 
4.3%
484 1
 
2.2%
1770 1
 
2.2%
735 1
 
2.2%
1926 1
 
2.2%
1331 1
 
2.2%
486 1
 
2.2%
987 1
 
2.2%
356 1
 
2.2%
Other values (28) 28
60.9%
ValueCountFrequency (%)
0 8
17.4%
2 1
 
2.2%
3 1
 
2.2%
6 1
 
2.2%
9 1
 
2.2%
11 1
 
2.2%
12 1
 
2.2%
23 1
 
2.2%
75 1
 
2.2%
89 1
 
2.2%
ValueCountFrequency (%)
73445 1
2.2%
66927 1
2.2%
48852 1
2.2%
14300 1
2.2%
10236 1
2.2%
8725 1
2.2%
5805 1
2.2%
5356 1
2.2%
2686 1
2.2%
1971 1
2.2%

Interactions

2023-12-13T02:37:22.644873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.306885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.761798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.211643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.743784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.424040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.886607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.320309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.857967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.548254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.005285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.437514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.971865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.645550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.114125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.534451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:37:25.430639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공항노선정기_부정기여부여객성인도착여객성인출발여객유아도착여객유아출발
공항1.0000.0000.0000.0000.0000.0000.000
노선0.0001.0000.0000.0000.0000.0000.000
정기_부정기여부0.0000.0001.0000.1600.1600.2150.447
여객성인도착0.0000.0000.1601.0001.0000.9990.921
여객성인출발0.0000.0000.1601.0001.0000.9990.921
여객유아도착0.0000.0000.2150.9990.9991.0001.000
여객유아출발0.0000.0000.4470.9210.9211.0001.000
2023-12-13T02:37:25.559542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정기_부정기여부노선공항
정기_부정기여부1.0000.0000.000
노선0.0001.0000.000
공항0.0000.0001.000
2023-12-13T02:37:25.684965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
여객성인도착여객성인출발여객유아도착여객유아출발공항노선정기_부정기여부
여객성인도착1.0000.9890.9820.9720.0000.0000.184
여객성인출발0.9891.0000.9760.9820.0000.0000.184
여객유아도착0.9820.9761.0000.9910.0000.0000.250
여객유아출발0.9720.9820.9911.0000.0000.0000.292
공항0.0000.0000.0000.0001.0000.0000.000
노선0.0000.0000.0000.0000.0001.0000.000
정기_부정기여부0.1840.1840.2500.2920.0000.0001.000

Missing values

2023-12-13T02:37:23.148247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:37:23.305051image/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

연도공항노선정기_부정기여부여객성인도착여객성인출발여객유아도착여객유아출발
02023인천 INCHEON국내선정기8396484258496484
12023인천 INCHEON국제선정기14237886142972955898566927
22023김포 GIMPO국내선정기566572156674624797148852
32023김포 GIMPO국제선정기66296966449826172686
42023김해 GIMHAE국내선정기200268320329591412914300
52023김해 GIMHAE국제선정기1621229162015683108725
62023제주 JEJU국내선정기791907578630057509173445
72023제주 JEJU국제선정기23758522644210751022
82023대구 DAEGU국내선정기64673164718057235805
92023대구 DAEGU국제선정기25249425422110721017
연도공항노선정기_부정기여부여객성인도착여객성인출발여객유아도착여객유아출발
362023무안 MUAN국제선부정기4991950734113116
372023양양 YANGYANG국내선부정기1344010
382023양양 YANGYANG국제선부정기93276722
392023광주 GWANGJU국내선부정기33871093
402023여수 YEOSU국내선부정기85181096
412023울산 ULSAN국내선부정기32235069
422023사천 SACHEON국내선부정기0000
432023포항 경주 POHANG GYEONGJU국내선부정기0000
442023군산 GUNSAN국내선부정기0000
452023원주 WONJU국내선부정기0000