Overview

Dataset statistics

Number of variables7
Number of observations26
Missing cells20
Missing cells (%)11.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory68.1 B

Variable types

Numeric7

Dataset

Description농업인 자녀 대학 학자금 집계 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=KL49562DZ28VBXQITZ3U12537513&infSeq=1

Alerts

집계년도 is highly overall correlated with 인원수(명) and 1 other fieldsHigh correlation
인원수(명) is highly overall correlated with 집계년도 and 2 other fieldsHigh correlation
총지원액(천원) is highly overall correlated with 인원수(명) and 4 other fieldsHigh correlation
국비(천원) is highly overall correlated with 총지원액(천원) and 2 other fieldsHigh correlation
도비(천원) is highly overall correlated with 인원수(명) and 3 other fieldsHigh correlation
시군비(천원) is highly overall correlated with 총지원액(천원) and 3 other fieldsHigh correlation
연간지원액(천원) is highly overall correlated with 집계년도 and 2 other fieldsHigh correlation
국비(천원) has 14 (53.8%) missing valuesMissing
연간지원액(천원) has 6 (23.1%) missing valuesMissing
집계년도 has unique valuesUnique
인원수(명) has unique valuesUnique
총지원액(천원) has unique valuesUnique
도비(천원) has unique valuesUnique
시군비(천원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:31:02.453690
Analysis finished2023-12-10 22:31:07.085050
Duration4.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.5
Minimum1993
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:31:07.398560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1993
5-th percentile1994.25
Q11999.25
median2005.5
Q32011.75
95-th percentile2016.75
Maximum2018
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.6485293
Coefficient of variation (CV)0.0038137767
Kurtosis-1.2
Mean2005.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum52143
Variance58.5
MonotonicityStrictly decreasing
2023-12-11T07:31:07.511912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2018 1
 
3.8%
2004 1
 
3.8%
1993 1
 
3.8%
1994 1
 
3.8%
1995 1
 
3.8%
1996 1
 
3.8%
1997 1
 
3.8%
1998 1
 
3.8%
1999 1
 
3.8%
2000 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1993 1
3.8%
1994 1
3.8%
1995 1
3.8%
1996 1
3.8%
1997 1
3.8%
1998 1
3.8%
1999 1
3.8%
2000 1
3.8%
2001 1
3.8%
2002 1
3.8%
ValueCountFrequency (%)
2018 1
3.8%
2017 1
3.8%
2016 1
3.8%
2015 1
3.8%
2014 1
3.8%
2013 1
3.8%
2012 1
3.8%
2011 1
3.8%
2010 1
3.8%
2009 1
3.8%

인원수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7099.6923
Minimum3279
Maximum12745
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:31:07.619566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3279
5-th percentile3744.75
Q14775.25
median7014
Q38963
95-th percentile10312.5
Maximum12745
Range9466
Interquartile range (IQR)4187.75

Descriptive statistics

Standard deviation2501.4746
Coefficient of variation (CV)0.35233563
Kurtosis-0.74777705
Mean7099.6923
Median Absolute Deviation (MAD)2195.5
Skewness0.18231585
Sum184592
Variance6257375
MonotonicityNot monotonic
2023-12-11T07:31:07.731908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3279 1
 
3.8%
6849 1
 
3.8%
12745 1
 
3.8%
9248 1
 
3.8%
10445 1
 
3.8%
8653 1
 
3.8%
9018 1
 
3.8%
8302 1
 
3.8%
7179 1
 
3.8%
6021 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
3279 1
3.8%
3730 1
3.8%
3789 1
3.8%
4098 1
3.8%
4295 1
3.8%
4354 1
3.8%
4748 1
3.8%
4857 1
3.8%
5620 1
3.8%
6021 1
3.8%
ValueCountFrequency (%)
12745 1
3.8%
10445 1
3.8%
9915 1
3.8%
9404 1
3.8%
9271 1
3.8%
9248 1
3.8%
9018 1
3.8%
8798 1
3.8%
8691 1
3.8%
8653 1
3.8%

총지원액(천원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5352549.9
Minimum1036242
Maximum9535878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:31:07.842704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1036242
5-th percentile1607079
Q14051333.5
median4881645.5
Q36770242.2
95-th percentile9362645.8
Maximum9535878
Range8499636
Interquartile range (IQR)2718908.8

Descriptive statistics

Standard deviation2518892.4
Coefficient of variation (CV)0.47059671
Kurtosis-0.765763
Mean5352549.9
Median Absolute Deviation (MAD)1476256
Skewness0.27681588
Sum1.391663 × 108
Variance6.3448187 × 1012
MonotonicityNot monotonic
2023-12-11T07:31:07.944390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1036242 1
 
3.8%
6155018 1
 
3.8%
4309276 1
 
3.8%
4137834 1
 
3.8%
4780472 1
 
3.8%
4861291 1
 
3.8%
5532696 1
 
3.8%
4902000 1
 
3.8%
4500513 1
 
3.8%
4022500 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1036242 1
3.8%
1473548 1
3.8%
2007672 1
3.8%
2655758 1
3.8%
2800880 1
3.8%
3202506 1
3.8%
4022500 1
3.8%
4137834 1
3.8%
4309276 1
3.8%
4500513 1
3.8%
ValueCountFrequency (%)
9535878 1
3.8%
9373011 1
3.8%
9331550 1
3.8%
8917683 1
3.8%
8876087 1
3.8%
8648733 1
3.8%
6975317 1
3.8%
6155018 1
3.8%
5888969 1
3.8%
5532696 1
3.8%

국비(천원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing14
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean1411213.6
Minimum840263
Maximum1844742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:31:08.048170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum840263
5-th percentile906266.3
Q11314307
median1452818
Q31611303.2
95-th percentile1743030.5
Maximum1844742
Range1004479
Interquartile range (IQR)296996.25

Descriptive statistics

Standard deviation291427.22
Coefficient of variation (CV)0.20650823
Kurtosis0.17069696
Mean1411213.6
Median Absolute Deviation (MAD)161527.5
Skewness-0.74712231
Sum16934563
Variance8.4929827 × 1010
MonotonicityNot monotonic
2023-12-11T07:31:08.188570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1844742 1
 
3.8%
1608261 1
 
3.8%
840263 1
 
3.8%
960269 1
 
3.8%
1206766 1
 
3.8%
1350154 1
 
3.8%
1469388 1
 
3.8%
1659812 1
 
3.8%
1620430 1
 
3.8%
1559398 1
 
3.8%
Other values (2) 2
 
7.7%
(Missing) 14
53.8%
ValueCountFrequency (%)
840263 1
3.8%
960269 1
3.8%
1206766 1
3.8%
1350154 1
3.8%
1378832 1
3.8%
1436248 1
3.8%
1469388 1
3.8%
1559398 1
3.8%
1608261 1
3.8%
1620430 1
3.8%
ValueCountFrequency (%)
1844742 1
3.8%
1659812 1
3.8%
1620430 1
3.8%
1608261 1
3.8%
1559398 1
3.8%
1469388 1
3.8%
1436248 1
3.8%
1378832 1
3.8%
1350154 1
3.8%
1206766 1
3.8%

도비(천원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1550207
Minimum310866
Maximum2383970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:31:08.328035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum310866
5-th percentile482089.75
Q11208160.5
median1592471.5
Q32098259.8
95-th percentile2338828
Maximum2383970
Range2073104
Interquartile range (IQR)890099.25

Descriptive statistics

Standard deviation596768.25
Coefficient of variation (CV)0.38496037
Kurtosis-0.54319948
Mean1550207
Median Absolute Deviation (MAD)441375
Skewness-0.44559556
Sum40305381
Variance3.5613235 × 1011
MonotonicityNot monotonic
2023-12-11T07:31:08.490402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
310866 1
 
3.8%
2152199 1
 
3.8%
1436250 1
 
3.8%
1378832 1
 
3.8%
1609763 1
 
3.8%
1620431 1
 
3.8%
1936442 1
 
3.8%
1716306 1
 
3.8%
1575180 1
 
3.8%
1407867 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
310866 1
3.8%
442021 1
3.8%
602296 1
3.8%
796771 1
3.8%
980309 1
3.8%
1121118 1
3.8%
1181075 1
3.8%
1289417 1
3.8%
1378832 1
3.8%
1407867 1
3.8%
ValueCountFrequency (%)
2383970 1
3.8%
2343253 1
3.8%
2325553 1
3.8%
2229420 1
3.8%
2219022 1
3.8%
2154493 1
3.8%
2152199 1
3.8%
1936442 1
3.8%
1876303 1
3.8%
1743982 1
3.8%

시군비(천원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3151013.6
Minimum725376
Maximum7151908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:31:08.624928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum725376
5-th percentile993112.75
Q11415094.8
median1867644.5
Q35027683
95-th percentile7023817.8
Maximum7151908
Range6426532
Interquartile range (IQR)3612588.2

Descriptive statistics

Standard deviation2322659
Coefficient of variation (CV)0.73711489
Kurtosis-1.0918948
Mean3151013.6
Median Absolute Deviation (MAD)791321.5
Skewness0.79947452
Sum81926354
Variance5.3947451 × 1012
MonotonicityNot monotonic
2023-12-11T07:31:08.770138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
725376 1
 
3.8%
2158077 1
 
3.8%
1436778 1
 
3.8%
1380170 1
 
3.8%
1611311 1
 
3.8%
1620430 1
 
3.8%
1936442 1
 
3.8%
1716306 1
 
3.8%
1575179 1
 
3.8%
1407867 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
725376 1
3.8%
980308 1
3.8%
1031527 1
3.8%
1121119 1
3.8%
1380170 1
3.8%
1405376 1
3.8%
1407867 1
3.8%
1436778 1
3.8%
1575179 1
3.8%
1611311 1
3.8%
ValueCountFrequency (%)
7151908 1
3.8%
7029758 1
3.8%
7005997 1
3.8%
6688263 1
3.8%
6657065 1
3.8%
6494240 1
3.8%
5231335 1
3.8%
4416727 1
3.8%
3866281 1
3.8%
3543225 1
3.8%

연간지원액(천원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)75.0%
Missing6
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean889.05
Minimum465
Maximum1180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:31:08.955801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum465
5-th percentile557.15
Q1644
median986
Q31100
95-th percentile1180
Maximum1180
Range715
Interquartile range (IQR)456

Descriptive statistics

Standard deviation250.01231
Coefficient of variation (CV)0.28121288
Kurtosis-1.6625667
Mean889.05
Median Absolute Deviation (MAD)194
Skewness-0.30267147
Sum17781
Variance62506.155
MonotonicityNot monotonic
2023-12-11T07:31:09.084916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1100 4
15.4%
1180 2
 
7.7%
644 2
 
7.7%
1169 1
 
3.8%
1113 1
 
3.8%
1060 1
 
3.8%
1010 1
 
3.8%
962 1
 
3.8%
792 1
 
3.8%
758 1
 
3.8%
Other values (5) 5
19.2%
(Missing) 6
23.1%
ValueCountFrequency (%)
465 1
3.8%
562 1
3.8%
592 1
3.8%
598 1
3.8%
644 2
7.7%
652 1
3.8%
758 1
3.8%
792 1
3.8%
962 1
3.8%
1010 1
3.8%
ValueCountFrequency (%)
1180 2
7.7%
1169 1
 
3.8%
1113 1
 
3.8%
1100 4
15.4%
1060 1
 
3.8%
1010 1
 
3.8%
962 1
 
3.8%
792 1
 
3.8%
758 1
 
3.8%
652 1
 
3.8%

Interactions

2023-12-11T07:31:06.190516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:02.640342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.272900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.821228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.395183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.071979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.628141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:06.288370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:02.715223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.363197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.893017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.502761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.154047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.718978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:06.371547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:02.789837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.435529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.964452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.603961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.226083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.799253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:06.455199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:02.895323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.503196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.040638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.717585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.309715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.873135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:06.529361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:02.983040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.585047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.115298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.814140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.388036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.953200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:06.618092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.054024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.653852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.183138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.898620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.460913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:06.019873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:06.702802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.146087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:03.727571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.279040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:04.983219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:05.541705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:06.105166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:31:09.202447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도인원수(명)총지원액(천원)국비(천원)도비(천원)시군비(천원)연간지원액(천원)
집계년도1.0000.7220.6720.5160.8160.6700.769
인원수(명)0.7221.0000.5020.4900.3930.3290.652
총지원액(천원)0.6720.5021.0000.8220.9440.8790.587
국비(천원)0.5160.4900.8221.0000.8221.0000.460
도비(천원)0.8160.3930.9440.8221.0000.8520.697
시군비(천원)0.6700.3290.8791.0000.8521.0000.675
연간지원액(천원)0.7690.6520.5870.4600.6970.6751.000
2023-12-11T07:31:09.357505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도인원수(명)총지원액(천원)국비(천원)도비(천원)시군비(천원)연간지원액(천원)
집계년도1.000-0.627-0.077-0.049-0.2770.1710.894
인원수(명)-0.6271.0000.6360.4550.7510.495-0.035
총지원액(천원)-0.0770.6361.0000.9440.9470.9350.644
국비(천원)-0.0490.4550.9441.0000.9300.930-0.085
도비(천원)-0.2770.7510.9470.9301.0000.8260.407
시군비(천원)0.1710.4950.9350.9300.8261.0000.768
연간지원액(천원)0.894-0.0350.644-0.0850.4070.7681.000

Missing values

2023-12-11T07:31:06.834672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:31:06.940923image/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.
2023-12-11T07:31:07.037835image/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

집계년도인원수(명)총지원액(천원)국비(천원)도비(천원)시군비(천원)연간지원액(천원)
0201832791036242<NA>310866725376<NA>
1201737891473548<NA>4420211031527<NA>
2201640982007672<NA>6022961405376<NA>
3201543542655758<NA>7967711858987<NA>
4201442954724300<NA>118107535432251100
5201348575155698<NA>128941738662811100
6201256205888969<NA>147224244167271100
7201167096975317<NA>174398252313351100
8201083648648733<NA>215449364942401180
9200986918917683<NA>222942066882631180
집계년도인원수(명)총지원액(천원)국비(천원)도비(천원)시군비(천원)연간지원액(천원)
16200237302800880840263980309980308758
1720014748320250696026911211181121119652
18200060214022500120676614078671407867598
19199971794500513135015415751801575179644
20199883024902000146938817163061716306644
21199790185532696165981219364421936442592
22199686534861291162043016204311620430562
231995104454780472155939816097631611311465
24199492484137834137883213788321380170<NA>
251993127454309276143624814362501436778<NA>