Overview

Dataset statistics

Number of variables8
Number of observations95
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory72.4 B

Variable types

Numeric7
Categorical1

Dataset

Description- 분기별 총사육두수, 경산우두수, 착유우두수, 낙농가수 등의 가축 사육 통계를 제공합니다. - 단위: 두수(두), 낙농가수(호), 호당생산량(kg) - 데이터 제공처: 낙농진흥회
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/853

Alerts

연도 is highly overall correlated with 총사육두수 and 5 other fieldsHigh correlation
총사육두수 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
경산우두수 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
착유우두수 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
낙농가수 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
호당사육두수 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
호당생산량 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
총사육두수 has unique valuesUnique
호당생산량 has unique valuesUnique

Reproduction

Analysis started2023-12-11 20:01:10.252205
Analysis finished2023-12-11 20:01:14.330235
Duration4.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.3789
Minimum2000
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T05:01:14.378791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12005.5
median2011
Q32017
95-th percentile2022
Maximum2023
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.8930723
Coefficient of variation (CV)0.0034270381
Kurtosis-1.1995815
Mean2011.3789
Median Absolute Deviation (MAD)6
Skewness0.0033606749
Sum191081
Variance47.514446
MonotonicityIncreasing
2023-12-12T05:01:14.489897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2000 4
 
4.2%
2001 4
 
4.2%
2022 4
 
4.2%
2021 4
 
4.2%
2020 4
 
4.2%
2019 4
 
4.2%
2018 4
 
4.2%
2017 4
 
4.2%
2016 4
 
4.2%
2015 4
 
4.2%
Other values (14) 55
57.9%
ValueCountFrequency (%)
2000 4
4.2%
2001 4
4.2%
2002 4
4.2%
2003 4
4.2%
2004 4
4.2%
2005 4
4.2%
2006 4
4.2%
2007 4
4.2%
2008 4
4.2%
2009 4
4.2%
ValueCountFrequency (%)
2023 3
3.2%
2022 4
4.2%
2021 4
4.2%
2020 4
4.2%
2019 4
4.2%
2018 4
4.2%
2017 4
4.2%
2016 4
4.2%
2015 4
4.2%
2014 4
4.2%

분기
Categorical

Distinct4
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size892.0 B
1분기
24 
2분기
24 
3분기
24 
4분기
23 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1분기
2nd row2분기
3rd row3분기
4th row4분기
5th row1분기

Common Values

ValueCountFrequency (%)
1분기 24
25.3%
2분기 24
25.3%
3분기 24
25.3%
4분기 23
24.2%

Length

2023-12-12T05:01:14.601470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:01:14.698314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1분기 24
25.3%
2분기 24
25.3%
3분기 24
25.3%
4분기 23
24.2%

총사육두수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446571.37
Minimum382642
Maximum551890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T05:01:14.813654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum382642
5-th percentile389819.8
Q1406541.5
median424662
Q3480325
95-th percentile544412
Maximum551890
Range169248
Interquartile range (IQR)73783.5

Descriptive statistics

Standard deviation52213.705
Coefficient of variation (CV)0.1169213
Kurtosis-0.63167725
Mean446571.37
Median Absolute Deviation (MAD)23131
Skewness0.87529335
Sum42424280
Variance2.726271 × 109
MonotonicityNot monotonic
2023-12-12T05:01:14.946077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
536773 1
 
1.1%
542518 1
 
1.1%
411032 1
 
1.1%
402175 1
 
1.1%
399913 1
 
1.1%
404293 1
 
1.1%
407310 1
 
1.1%
402405 1
 
1.1%
408516 1
 
1.1%
411342 1
 
1.1%
Other values (85) 85
89.5%
ValueCountFrequency (%)
382642 1
1.1%
384873 1
1.1%
385699 1
1.1%
387996 1
1.1%
389726 1
1.1%
389860 1
1.1%
396466 1
1.1%
396723 1
1.1%
399680 1
1.1%
399745 1
1.1%
ValueCountFrequency (%)
551890 1
1.1%
550040 1
1.1%
548483 1
1.1%
548176 1
1.1%
545350 1
1.1%
544010 1
1.1%
543708 1
1.1%
543587 1
1.1%
543161 1
1.1%
542518 1
1.1%

경산우두수
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256322
Minimum221857
Maximum315656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T05:01:15.080933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum221857
5-th percentile226491.6
Q1238572
median246763
Q3271216
95-th percentile310541.6
Maximum315656
Range93799
Interquartile range (IQR)32644

Descriptive statistics

Standard deviation27571.299
Coefficient of variation (CV)0.10756509
Kurtosis-0.4264997
Mean256322
Median Absolute Deviation (MAD)13645
Skewness0.94011901
Sum24350590
Variance7.601765 × 108
MonotonicityNot monotonic
2023-12-12T05:01:15.201127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240575 2
 
2.1%
301274 1
 
1.1%
241668 1
 
1.1%
227792 1
 
1.1%
226494 1
 
1.1%
229029 1
 
1.1%
230529 1
 
1.1%
227714 1
 
1.1%
230639 1
 
1.1%
233118 1
 
1.1%
Other values (84) 84
88.4%
ValueCountFrequency (%)
221857 1
1.1%
224131 1
1.1%
225003 1
1.1%
226456 1
1.1%
226486 1
1.1%
226494 1
1.1%
226556 1
1.1%
227714 1
1.1%
227792 1
1.1%
227954 1
1.1%
ValueCountFrequency (%)
315656 1
1.1%
314093 1
1.1%
313834 1
1.1%
312393 1
1.1%
311159 1
1.1%
310277 1
1.1%
307893 1
1.1%
307544 1
1.1%
307387 1
1.1%
307347 1
1.1%

착유우두수
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216900.79
Minimum190063
Maximum270295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T05:01:15.315313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum190063
5-th percentile192101.3
Q1202798
median208205
Q3228821.5
95-th percentile262078
Maximum270295
Range80232
Interquartile range (IQR)26023.5

Descriptive statistics

Standard deviation22301.052
Coefficient of variation (CV)0.10281683
Kurtosis-0.32357835
Mean216900.79
Median Absolute Deviation (MAD)10539
Skewness0.95318022
Sum20605575
Variance4.9733691 × 108
MonotonicityNot monotonic
2023-12-12T05:01:15.436014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204008 2
 
2.1%
257785 1
 
1.1%
204934 1
 
1.1%
193498 1
 
1.1%
195902 1
 
1.1%
193608 1
 
1.1%
195998 1
 
1.1%
192487 1
 
1.1%
197890 1
 
1.1%
197105 1
 
1.1%
Other values (84) 84
88.4%
ValueCountFrequency (%)
190063 1
1.1%
190803 1
1.1%
190901 1
1.1%
192035 1
1.1%
192060 1
1.1%
192119 1
1.1%
192487 1
1.1%
192623 1
1.1%
193305 1
1.1%
193498 1
1.1%
ValueCountFrequency (%)
270295 1
1.1%
265130 1
1.1%
262998 1
1.1%
262677 1
1.1%
262554 1
1.1%
261874 1
1.1%
261343 1
1.1%
258040 1
1.1%
257785 1
1.1%
257104 1
1.1%

낙농가수
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7667.6526
Minimum5238
Maximum14110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T05:01:15.588786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5238
5-th percentile5462.7
Q15981.5
median6428
Q38920.5
95-th percentile13128
Maximum14110
Range8872
Interquartile range (IQR)2939

Descriptive statistics

Standard deviation2498.3557
Coefficient of variation (CV)0.32583058
Kurtosis0.27058095
Mean7667.6526
Median Absolute Deviation (MAD)715
Skewness1.2527045
Sum728427
Variance6241781.3
MonotonicityNot monotonic
2023-12-12T05:01:15.707448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5713 2
 
2.1%
6123 2
 
2.1%
14110 1
 
1.1%
5693 1
 
1.1%
5238 1
 
1.1%
5354 1
 
1.1%
5420 1
 
1.1%
5407 1
 
1.1%
5481 1
 
1.1%
5498 1
 
1.1%
Other values (83) 83
87.4%
ValueCountFrequency (%)
5238 1
1.1%
5256 1
1.1%
5354 1
1.1%
5407 1
1.1%
5420 1
1.1%
5481 1
1.1%
5498 1
1.1%
5587 1
1.1%
5632 1
1.1%
5633 1
1.1%
ValueCountFrequency (%)
14110 1
1.1%
13775 1
1.1%
13605 1
1.1%
13348 1
1.1%
13177 1
1.1%
13107 1
1.1%
13088 1
1.1%
12827 1
1.1%
12335 1
1.1%
12146 1
1.1%

호당사육두수
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.484211
Minimum38
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T05:01:15.814108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile41.7
Q154
median65
Q367
95-th percentile75.3
Maximum77
Range39
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.358328
Coefficient of variation (CV)0.16847135
Kurtosis-0.5431452
Mean61.484211
Median Absolute Deviation (MAD)6
Skewness-0.63987909
Sum5841
Variance107.29496
MonotonicityNot monotonic
2023-12-12T05:01:15.923012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
67 10
 
10.5%
66 9
 
9.5%
65 8
 
8.4%
62 6
 
6.3%
64 4
 
4.2%
76 4
 
4.2%
75 4
 
4.2%
74 3
 
3.2%
73 3
 
3.2%
50 3
 
3.2%
Other values (28) 41
43.2%
ValueCountFrequency (%)
38 1
1.1%
39 1
1.1%
40 1
1.1%
41 2
2.1%
42 2
2.1%
43 1
1.1%
44 1
1.1%
45 2
2.1%
46 1
1.1%
47 1
1.1%
ValueCountFrequency (%)
77 1
 
1.1%
76 4
4.2%
75 4
4.2%
74 3
3.2%
73 3
3.2%
72 1
 
1.1%
71 1
 
1.1%
70 2
2.1%
69 1
 
1.1%
68 3
3.2%

호당생산량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25020.168
Minimum13200
Maximum34809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T05:01:16.043224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13200
5-th percentile14658.3
Q120689.5
median26284
Q328908
95-th percentile32662.2
Maximum34809
Range21609
Interquartile range (IQR)8218.5

Descriptive statistics

Standard deviation5484.5822
Coefficient of variation (CV)0.21920645
Kurtosis-0.66931113
Mean25020.168
Median Absolute Deviation (MAD)3288
Skewness-0.48651752
Sum2376916
Variance30080642
MonotonicityNot monotonic
2023-12-12T05:01:16.156278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13682 1
 
1.1%
13811 1
 
1.1%
24865 1
 
1.1%
32756 1
 
1.1%
34809 1
 
1.1%
32137 1
 
1.1%
30958 1
 
1.1%
31710 1
 
1.1%
32924 1
 
1.1%
31917 1
 
1.1%
Other values (85) 85
89.5%
ValueCountFrequency (%)
13200 1
1.1%
13682 1
1.1%
13811 1
1.1%
14128 1
1.1%
14568 1
1.1%
14697 1
1.1%
15288 1
1.1%
15848 1
1.1%
16519 1
1.1%
17012 1
1.1%
ValueCountFrequency (%)
34809 1
1.1%
34226 1
1.1%
33121 1
1.1%
32924 1
1.1%
32756 1
1.1%
32622 1
1.1%
32282 1
1.1%
32137 1
1.1%
31917 1
1.1%
31710 1
1.1%

Interactions

2023-12-12T05:01:13.682983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:10.517064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.008110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.503611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.980758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.480020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.925538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.747248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:10.590546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.074265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.568324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.046653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.538548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.005069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.818815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:10.674412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.148079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.638771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.122514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.607765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.091839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.885345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:10.743622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.221588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.703042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.191918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.670374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.157701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.962157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:10.817338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.301353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.782464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.268075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.741717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.234694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:14.027267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:10.877979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.367696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.848191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.335747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.798510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.303610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:14.104698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:10.947044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.439890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:11.917211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.415196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:12.866489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:01:13.619510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:01:16.239609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도분기총사육두수경산우두수착유우두수낙농가수호당사육두수호당생산량
연도1.0000.0000.9500.9200.9060.8500.9470.940
분기0.0001.0000.0000.0000.0000.0000.0000.000
총사육두수0.9500.0001.0000.9610.9480.9510.9500.934
경산우두수0.9200.0000.9611.0000.9670.9640.9390.882
착유우두수0.9060.0000.9480.9671.0000.9330.9070.865
낙농가수0.8500.0000.9510.9640.9331.0000.9660.948
호당사육두수0.9470.0000.9500.9390.9070.9661.0000.939
호당생산량0.9400.0000.9340.8820.8650.9480.9391.000
2023-12-12T05:01:16.342493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도총사육두수경산우두수착유우두수낙농가수호당사육두수호당생산량분기
연도1.000-0.928-0.884-0.886-0.8100.7020.7820.000
총사육두수-0.9281.0000.9660.9590.822-0.696-0.7420.000
경산우두수-0.8840.9661.0000.9920.853-0.749-0.7620.000
착유우두수-0.8860.9590.9921.0000.854-0.750-0.7540.000
낙농가수-0.8100.8220.8530.8541.000-0.954-0.9540.000
호당사육두수0.702-0.696-0.749-0.750-0.9541.0000.9500.000
호당생산량0.782-0.742-0.762-0.754-0.9540.9501.0000.000
분기0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T05:01:14.192744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:01:14.292922image/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

연도분기총사육두수경산우두수착유우두수낙농가수호당사육두수호당생산량
020001분기536773301274257785141103813682
120002분기542518307893262554137753913811
220003분기541933307333257104136054013200
320004분기543708307387255053133484114128
420011분기540173307544265130131774115288
520012분기544010314093262998131074214568
620013분기550040315656261343130884214697
720014분기548176312393258040128274315848
820021분기548483313834270295123354418722
920022분기545350311159262677121464517346
연도분기총사육두수경산우두수착유우두수낙농가수호당사육두수호당생산량
8520212분기39968023348119799261076527926
8620213분기39974523334219787461336526464
8720214분기40079823124819609861056627450
8820221분기39672322999119503260876528724
8920222분기38799622645619203559916527350
9020223분기38972622648619206059576526486
9120224분기38986022500319080358886627377
9220231분기38487322413119006358106629319
9320232분기38264222655619211957136727864
9420233분기38569922795419330557136827143