Overview

Dataset statistics

Number of variables7
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory66.9 B

Variable types

Numeric7

Dataset

Description연도별 국내 항공수송실적 정보 제공합니다 (연도, 여객(명), 여객킬로, 화물, 화물톤킬로, 운항, 운항킬로 등)
URLhttps://www.data.go.kr/data/15052304/fileData.do

Alerts

구분 is highly overall correlated with 여객(명) and 3 other fieldsHigh correlation
여객(명) is highly overall correlated with 구분 and 3 other fieldsHigh correlation
여객킬로(Km) is highly overall correlated with 구분 and 3 other fieldsHigh correlation
화물(톤) is highly overall correlated with 화물톤킬로(Km)High correlation
화물톤킬로(Km) is highly overall correlated with 화물(톤)High correlation
운항(편) is highly overall correlated with 구분 and 3 other fieldsHigh correlation
운항킬로(Km) is highly overall correlated with 구분 and 3 other fieldsHigh correlation
구분 has unique valuesUnique
여객(명) has unique valuesUnique
여객킬로(Km) has unique valuesUnique
화물(톤) has unique valuesUnique
화물톤킬로(Km) has unique valuesUnique
운항(편) has unique valuesUnique
운항킬로(Km) has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:04:45.019104
Analysis finished2023-12-12 16:04:50.352262
Duration5.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.5
Minimum1989
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T01:04:50.419890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1990.65
Q11997.25
median2005.5
Q32013.75
95-th percentile2020.35
Maximum2022
Range33
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation9.9582462
Coefficient of variation (CV)0.004965468
Kurtosis-1.2
Mean2005.5
Median Absolute Deviation (MAD)8.5
Skewness0
Sum68187
Variance99.166667
MonotonicityStrictly increasing
2023-12-13T01:04:50.566725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1989 1
 
2.9%
2015 1
 
2.9%
2009 1
 
2.9%
2010 1
 
2.9%
2011 1
 
2.9%
2012 1
 
2.9%
2013 1
 
2.9%
2014 1
 
2.9%
2016 1
 
2.9%
2007 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1989 1
2.9%
1990 1
2.9%
1991 1
2.9%
1992 1
2.9%
1993 1
2.9%
1994 1
2.9%
1995 1
2.9%
1996 1
2.9%
1997 1
2.9%
1998 1
2.9%
ValueCountFrequency (%)
2022 1
2.9%
2021 1
2.9%
2020 1
2.9%
2019 1
2.9%
2018 1
2.9%
2017 1
2.9%
2016 1
2.9%
2015 1
2.9%
2014 1
2.9%
2013 1
2.9%

여객(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21881944
Minimum8951716
Maximum36328296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T01:04:50.724857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8951716
5-th percentile11836833
Q117401082
median21196434
Q325034913
95-th percentile33038955
Maximum36328296
Range27376580
Interquartile range (IQR)7633831

Descriptive statistics

Standard deviation6581870.7
Coefficient of variation (CV)0.30079004
Kurtosis-0.15850359
Mean21881944
Median Absolute Deviation (MAD)3991476.5
Skewness0.3843942
Sum7.4398608 × 108
Variance4.3321022 × 1013
MonotonicityNot monotonic
2023-12-13T01:04:50.866144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8951716 1
 
2.9%
27980134 1
 
2.9%
18061073 1
 
2.9%
20216355 1
 
2.9%
20980803 1
 
2.9%
21601518 1
 
2.9%
22353370 1
 
2.9%
24647538 1
 
2.9%
30912922 1
 
2.9%
16847870 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
8951716 1
2.9%
11063820 1
2.9%
12253071 1
2.9%
14554737 1
2.9%
15549988 1
2.9%
16847870 1
2.9%
16990360 1
2.9%
17157595 1
2.9%
17181085 1
2.9%
18061073 1
2.9%
ValueCountFrequency (%)
36328296 1
2.9%
33146646 1
2.9%
32980968 1
2.9%
32406255 1
2.9%
31600610 1
2.9%
30912922 1
2.9%
27980134 1
2.9%
25578653 1
2.9%
25164038 1
2.9%
24647538 1
2.9%

여객킬로(Km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1942967 × 109
Minimum3.1785758 × 109
Maximum1.3687479 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T01:04:50.993768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1785758 × 109
5-th percentile4.2942158 × 109
Q16.6449997 × 109
median7.9055367 × 109
Q39.3754624 × 109
95-th percentile1.2380749 × 1010
Maximum1.3687479 × 1010
Range1.0508903 × 1010
Interquartile range (IQR)2.7304627 × 109

Descriptive statistics

Standard deviation2.5302372 × 109
Coefficient of variation (CV)0.30878028
Kurtosis-0.17922481
Mean8.1942967 × 109
Median Absolute Deviation (MAD)1.287157 × 109
Skewness0.34875645
Sum2.7860609 × 1011
Variance6.4021003 × 1018
MonotonicityNot monotonic
2023-12-13T01:04:51.130911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3178575833 1
 
2.9%
10706669782 1
 
2.9%
7105171868 1
 
2.9%
8010586401 1
 
2.9%
8394805022 1
 
2.9%
8709800534 1
 
2.9%
9092997444 1
 
2.9%
9469617447 1
 
2.9%
11819784935 1
 
2.9%
6525889909 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
3178575833 1
2.9%
4010705187 1
2.9%
4446875398 1
2.9%
5233205955 1
2.9%
5510983478 1
2.9%
6481372976 1
2.9%
6525889909 1
2.9%
6593754843 1
2.9%
6643004388 1
2.9%
6650985647 1
2.9%
ValueCountFrequency (%)
13687479166 1
2.9%
12433233773 1
2.9%
12352487960 1
2.9%
12243233861 1
2.9%
11882156510 1
2.9%
11819784935 1
2.9%
10706669782 1
2.9%
9488378891 1
2.9%
9469617447 1
2.9%
9092997444 1
2.9%

화물(톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301029.38
Minimum154418
Maximum434228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T01:04:51.261387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum154418
5-th percentile182464.9
Q1255361.75
median285450
Q3361472.5
95-th percentile431616.5
Maximum434228
Range279810
Interquartile range (IQR)106110.75

Descriptive statistics

Standard deviation77911.173
Coefficient of variation (CV)0.25881584
Kurtosis-0.77733967
Mean301029.38
Median Absolute Deviation (MAD)49964
Skewness0.18534977
Sum10234999
Variance6.0701509 × 109
MonotonicityNot monotonic
2023-12-13T01:04:51.407210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
154418 1
 
2.9%
287781 1
 
2.9%
268677 1
 
2.9%
261857 1
 
2.9%
281132 1
 
2.9%
265277 1
 
2.9%
252686 1
 
2.9%
283119 1
 
2.9%
292887 1
 
2.9%
316398 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
154418 1
2.9%
181785 1
2.9%
182831 1
2.9%
199542 1
2.9%
204585 1
2.9%
229355 1
2.9%
241617 1
2.9%
252686 1
2.9%
254239 1
2.9%
258730 1
2.9%
ValueCountFrequency (%)
434228 1
2.9%
432702 1
2.9%
431032 1
2.9%
422565 1
2.9%
408985 1
2.9%
393275 1
2.9%
387319 1
2.9%
372385 1
2.9%
363547 1
2.9%
355249 1
2.9%

화물톤킬로(Km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1857018 × 108
Minimum63150670
Maximum1.7007137 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T01:04:51.556875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63150670
5-th percentile72112275
Q11.0331956 × 108
median1.1218719 × 108
Q31.4356842 × 108
95-th percentile1.6749619 × 108
Maximum1.7007137 × 108
Range1.069207 × 108
Interquartile range (IQR)40248864

Descriptive statistics

Standard deviation29995949
Coefficient of variation (CV)0.25298054
Kurtosis-0.76248221
Mean1.1857018 × 108
Median Absolute Deviation (MAD)19760435
Skewness0.17945234
Sum4.0313863 × 109
Variance8.9975698 × 1014
MonotonicityNot monotonic
2023-12-13T01:04:51.701807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
63150670 1
 
2.9%
112294507 1
 
2.9%
110838599 1
 
2.9%
106608636 1
 
2.9%
114880966 1
 
2.9%
108610122 1
 
2.9%
104239503 1
 
2.9%
110662644 1
 
2.9%
114658097 1
 
2.9%
128940422 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
63150670 1
2.9%
71541052 1
2.9%
72419857 1
2.9%
78577070 1
2.9%
80881521 1
2.9%
90403814 1
2.9%
94262745 1
2.9%
100289234 1
2.9%
103012912 1
2.9%
104239503 1
2.9%
ValueCountFrequency (%)
170071367 1
2.9%
167850697 1
2.9%
167305300 1
2.9%
166418231 1
2.9%
163527869 1
2.9%
151080785 1
2.9%
150594497 1
2.9%
148610923 1
2.9%
144816912 1
2.9%
139822961 1
2.9%

운항(편)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154114.03
Minimum78638
Maximum216445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T01:04:52.154521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78638
5-th percentile89064.65
Q1137762
median155223.5
Q3171361
95-th percentile202202.25
Maximum216445
Range137807
Interquartile range (IQR)33599

Descriptive statistics

Standard deviation32725.517
Coefficient of variation (CV)0.21234613
Kurtosis0.30378117
Mean154114.03
Median Absolute Deviation (MAD)16858.5
Skewness-0.36533183
Sum5239877
Variance1.0709595 × 109
MonotonicityNot monotonic
2023-12-13T01:04:52.286398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
78638 1
 
2.9%
182583 1
 
2.9%
147383 1
 
2.9%
146608 1
 
2.9%
151512 1
 
2.9%
155609 1
 
2.9%
161750 1
 
2.9%
170101 1
 
2.9%
190997 1
 
2.9%
133288 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
78638 1
2.9%
86087 1
2.9%
90668 1
2.9%
111264 1
2.9%
127046 1
2.9%
127771 1
2.9%
132567 1
2.9%
133288 1
2.9%
137092 1
2.9%
139772 1
2.9%
ValueCountFrequency (%)
216445 1
2.9%
212690 1
2.9%
196555 1
2.9%
195349 1
2.9%
194432 1
2.9%
190997 1
2.9%
182583 1
2.9%
172383 1
2.9%
171781 1
2.9%
170101 1
2.9%

운항킬로(Km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56742920
Minimum27769643
Maximum80598677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T01:04:52.430813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27769643
5-th percentile32115482
Q150449300
median57571050
Q363619260
95-th percentile74673145
Maximum80598677
Range52829034
Interquartile range (IQR)13169960

Descriptive statistics

Standard deviation12467444
Coefficient of variation (CV)0.21971806
Kurtosis0.26817274
Mean56742920
Median Absolute Deviation (MAD)6798294
Skewness-0.38597914
Sum1.9292593 × 109
Variance1.5543716 × 1014
MonotonicityNot monotonic
2023-12-13T01:04:52.570247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
27769643 1
 
2.9%
68317092 1
 
2.9%
57119912 1
 
2.9%
57160384 1
 
2.9%
59818746 1
 
2.9%
61719336 1
 
2.9%
64559278 1
 
2.9%
63554855 1
 
2.9%
71694540 1
 
2.9%
50962690 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
27769643 1
2.9%
30934763 1
2.9%
32751254 1
2.9%
40005141 1
2.9%
46936144 1
2.9%
47789782 1
2.9%
47852712 1
2.9%
47948929 1
2.9%
50278170 1
2.9%
50962690 1
2.9%
ValueCountFrequency (%)
80598677 1
2.9%
78207246 1
2.9%
72770168 1
2.9%
71936173 1
2.9%
71694540 1
2.9%
71323490 1
2.9%
68317092 1
2.9%
64559278 1
2.9%
63640729 1
2.9%
63554855 1
2.9%

Interactions

2023-12-13T01:04:49.420058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.202767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.786782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:46.721412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.311733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.917629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:48.741645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.529590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.278547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.866830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:46.801301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.398490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:48.054931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:48.845574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.610964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.365831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.969362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:46.881756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.481701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:48.166769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:48.935596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.701064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.464389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:46.391164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:46.958082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.564443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:48.294611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.024234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.801814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.544170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:46.463880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.052053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.648536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:48.395125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.146407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.886527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.621701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:46.541132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.136739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.730115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:48.495168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.232331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.999622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:45.708120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:46.625712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.220934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:47.815054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:48.630121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:04:49.327513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:04:52.693350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(편)운항킬로(Km)
구분1.0000.6220.8690.7920.7710.7900.778
여객(명)0.6221.0000.9260.6770.6290.9220.912
여객킬로(Km)0.8690.9261.0000.8550.6800.8640.879
화물(톤)0.7920.6770.8551.0000.9570.5850.516
화물톤킬로(Km)0.7710.6290.6800.9571.0000.5230.524
운항(편)0.7900.9220.8640.5850.5231.0000.988
운항킬로(Km)0.7780.9120.8790.5160.5240.9881.000
2023-12-13T01:04:52.855742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(편)운항킬로(Km)
구분1.0000.7280.832-0.205-0.1810.7440.868
여객(명)0.7281.0000.9710.0930.0620.9680.923
여객킬로(Km)0.8320.9711.000-0.021-0.0320.9420.960
화물(톤)-0.2050.093-0.0211.0000.9870.096-0.045
화물톤킬로(Km)-0.1810.062-0.0320.9871.0000.064-0.047
운항(편)0.7440.9680.9420.0960.0641.0000.954
운항킬로(Km)0.8680.9230.960-0.045-0.0470.9541.000

Missing values

2023-12-13T01:04:50.157005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:04:50.296141image/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

구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(편)운항킬로(Km)
0198989517163178575833154418631506707863827769643
11990110638204010705187182831715410528608730934763
21991122530714446875398199542785770709066832751254
319921455473752332059552416179426274511126440005141
4199315549988551098347827331410488746213256746936144
5199418405866648137297630608211606266913709247948929
6199521008531740554233032272012286781914414750278170
7199623566588828767728935136313378361515483853555899
8199725578653905205695938731914861092317178159742408
9199819504413687686633936354713982296115738754326734
구분여객(명)여객킬로(Km)화물(톤)화물톤킬로(Km)운항(편)운항킬로(Km)
24201322353370909299744425268610423950316175064559278
25201424647538946961744728311911066264417010163554855
262015279801341070666978228778111229450718258368317092
272016309129221181978493529288711465809719099771694540
282017324062551224323386129012511207986519655572770168
292018316006101188215651027319210533292919443271323490
302019329809681243323377325873010028923419534971936173
3120202516403894883788911817857241985717238363640729
32202133146646123524879602045858088152121269078207246
33202236328296136874791662293559040381421644580598677