Overview

Dataset statistics

Number of variables6
Number of observations145
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory54.9 B

Variable types

Numeric6

Dataset

Description농작물 영농과정 정보 및 농업경영회계 통계정보 제공
Author농림수산식품교육문화정보원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220210000000001812

Alerts

YM is highly overall correlated with IMPORT_TOT_AMT and 1 other fieldsHigh correlation
IMPORT_TOT_AMT is highly overall correlated with YM and 3 other fieldsHigh correlation
IMPORT_AVRG_AMT is highly overall correlated with IMPORT_TOT_AMTHigh correlation
EXPNDTR_TOT_AMT is highly overall correlated with IMPORT_TOT_AMT and 1 other fieldsHigh correlation
EXPNDTR_AVRG_AMT is highly overall correlated with EXPNDTR_TOT_AMTHigh correlation
YY is highly overall correlated with YM and 1 other fieldsHigh correlation
YM has unique valuesUnique
IMPORT_TOT_AMT has 14 (9.7%) zerosZeros
IMPORT_AVRG_AMT has 14 (9.7%) zerosZeros
EXPNDTR_TOT_AMT has 14 (9.7%) zerosZeros
EXPNDTR_AVRG_AMT has 14 (9.7%) zerosZeros

Reproduction

Analysis started2023-12-11 03:46:21.663258
Analysis finished2023-12-11 03:46:26.638732
Duration4.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

YM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200986.64
Minimum199803
Maximum201611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T12:46:26.731602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum199803
5-th percentile200029.8
Q1200711
median201011
Q3201311
95-th percentile201603.8
Maximum201611
Range1808
Interquartile range (IQR)600

Descriptive statistics

Standard deviation451.54281
Coefficient of variation (CV)0.002246631
Kurtosis-0.07387738
Mean200986.64
Median Absolute Deviation (MAD)300
Skewness-0.71374987
Sum29143063
Variance203890.91
MonotonicityNot monotonic
2023-12-11T12:46:26.881302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201403 1
 
0.7%
199908 1
 
0.7%
200503 1
 
0.7%
200412 1
 
0.7%
200408 1
 
0.7%
200407 1
 
0.7%
200404 1
 
0.7%
200312 1
 
0.7%
200308 1
 
0.7%
200212 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
199803 1
0.7%
199808 1
0.7%
199904 1
0.7%
199908 1
0.7%
200002 1
0.7%
200005 1
0.7%
200011 1
0.7%
200012 1
0.7%
200101 1
0.7%
200107 1
0.7%
ValueCountFrequency (%)
201611 1
0.7%
201610 1
0.7%
201609 1
0.7%
201608 1
0.7%
201607 1
0.7%
201606 1
0.7%
201605 1
0.7%
201604 1
0.7%
201603 1
0.7%
201602 1
0.7%

IMPORT_TOT_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2222724 × 1010
Minimum0
Maximum1.1230385 × 1012
Zeros14
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T12:46:27.036567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140000000
median7.0507592 × 108
Q34.0297211 × 109
95-th percentile2.2026864 × 1010
Maximum1.1230385 × 1012
Range1.1230385 × 1012
Interquartile range (IQR)3.9897211 × 109

Descriptive statistics

Standard deviation9.3531115 × 1010
Coefficient of variation (CV)7.6522316
Kurtosis140.98047
Mean1.2222724 × 1010
Median Absolute Deviation (MAD)7.0507592 × 108
Skewness11.800918
Sum1.772295 × 1012
Variance8.7480696 × 1021
MonotonicityNot monotonic
2023-12-11T12:46:27.186222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
9.7%
2500000 2
 
1.4%
4000000 2
 
1.4%
90000000 2
 
1.4%
163898000 1
 
0.7%
195198400 1
 
0.7%
1132133 1
 
0.7%
184068200 1
 
0.7%
30000000 1
 
0.7%
16380000 1
 
0.7%
Other values (119) 119
82.1%
ValueCountFrequency (%)
0 14
9.7%
1132133 1
 
0.7%
1200000 1
 
0.7%
1500000 1
 
0.7%
2000000 1
 
0.7%
2479881 1
 
0.7%
2500000 2
 
1.4%
2800000 1
 
0.7%
4000000 2
 
1.4%
4281328 1
 
0.7%
ValueCountFrequency (%)
1123038452854 1
0.7%
98128911669 1
0.7%
58334384128 1
0.7%
35552239895 1
0.7%
34520313352 1
0.7%
25767044212 1
0.7%
22511263794 1
0.7%
22061482721 1
0.7%
21888387944 1
0.7%
17230440497 1
0.7%

IMPORT_AVRG_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct130
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28422028
Minimum0
Maximum9.1378231 × 108
Zeros14
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T12:46:27.320130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12566500
median6078758.3
Q316241140
95-th percentile1.626184 × 108
Maximum9.1378231 × 108
Range9.1378231 × 108
Interquartile range (IQR)13674640

Descriptive statistics

Standard deviation89146552
Coefficient of variation (CV)3.1365303
Kurtosis68.800681
Mean28422028
Median Absolute Deviation (MAD)4078758.3
Skewness7.4668283
Sum4.1211941 × 109
Variance7.9471078 × 1015
MonotonicityNot monotonic
2023-12-11T12:46:27.459865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
9.7%
2500000.0 2
 
1.4%
2000000.0 2
 
1.4%
10000000.0 1
 
0.7%
2800000.0 1
 
0.7%
50000000.0 1
 
0.7%
195198400.0 1
 
0.7%
1132133.0 1
 
0.7%
184068200.0 1
 
0.7%
163898000.0 1
 
0.7%
Other values (120) 120
82.8%
ValueCountFrequency (%)
0.0 14
9.7%
150000.0 1
 
0.7%
333333.333333 1
 
0.7%
407692.307692 1
 
0.7%
428132.8 1
 
0.7%
1132133.0 1
 
0.7%
1493169.230769 1
 
0.7%
1500000.0 1
 
0.7%
1608681.0 1
 
0.7%
1934101.255391 1
 
0.7%
ValueCountFrequency (%)
913782305.007323 1
0.7%
280300800.0 1
0.7%
267880000.0 1
0.7%
251779000.0 1
0.7%
195198400.0 1
0.7%
184068200.0 1
0.7%
177027027.027027 1
0.7%
163898000.0 1
0.7%
157500000.0 1
0.7%
136898435.5 1
0.7%

EXPNDTR_TOT_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct131
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1383924 × 1015
Minimum0
Maximum3.0006667 × 1017
Zeros14
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T12:46:27.597468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130404806
median1.597854 × 108
Q32.0593358 × 109
95-th percentile6.528147 × 109
Maximum3.0006667 × 1017
Range3.0006667 × 1017
Interquartile range (IQR)2.028931 × 109

Descriptive statistics

Standard deviation2.492725 × 1016
Coefficient of variation (CV)11.657005
Kurtosis144.67161
Mean2.1383924 × 1015
Median Absolute Deviation (MAD)1.582854 × 108
Skewness12.021719
Sum3.1006689 × 1017
Variance6.2136778 × 1032
MonotonicityNot monotonic
2023-12-11T12:46:27.745466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
9.7%
17000000 2
 
1.4%
388376815 1
 
0.7%
252390320 1
 
0.7%
8483600 1
 
0.7%
161504109 1
 
0.7%
236300800 1
 
0.7%
25000000 1
 
0.7%
164712000 1
 
0.7%
5000 1
 
0.7%
Other values (121) 121
83.4%
ValueCountFrequency (%)
0 14
9.7%
5000 1
 
0.7%
15000 1
 
0.7%
72000 1
 
0.7%
211394 1
 
0.7%
500000 1
 
0.7%
1300000 1
 
0.7%
1500000 1
 
0.7%
2426400 1
 
0.7%
7950000 1
 
0.7%
ValueCountFrequency (%)
300066666808508660 1
0.7%
10000000037651380 1
0.7%
36017311911 1
0.7%
23501390415 1
0.7%
13245452211 1
0.7%
7883782360 1
0.7%
7839373279 1
0.7%
6618830685 1
0.7%
6165412132 1
0.7%
5962441447 1
0.7%

EXPNDTR_AVRG_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct131
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.29062 × 1012
Minimum0
Maximum1.7455885 × 1014
Zeros14
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T12:46:27.910194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1104680.51
median4672091.4
Q310855029
95-th percentile1.312701 × 108
Maximum1.7455885 × 1014
Range1.7455885 × 1014
Interquartile range (IQR)10750348

Descriptive statistics

Standard deviation1.4526673 × 1013
Coefficient of variation (CV)11.255577
Kurtosis143.47545
Mean1.29062 × 1012
Median Absolute Deviation (MAD)4625565.3
Skewness11.951495
Sum1.871399 × 1014
Variance2.1102422 × 1026
MonotonicityNot monotonic
2023-12-11T12:46:28.508254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
9.7%
17000000.0 2
 
1.4%
1631835.357142 1
 
0.7%
126195160.0 1
 
0.7%
8483600.0 1
 
0.7%
80752054.5 1
 
0.7%
236300800.0 1
 
0.7%
12500000.0 1
 
0.7%
164712000.0 1
 
0.7%
5000.0 1
 
0.7%
Other values (121) 121
83.4%
ValueCountFrequency (%)
0.0 14
9.7%
5000.0 1
 
0.7%
7500.0 1
 
0.7%
13413.841807 1
 
0.7%
13747.145061 1
 
0.7%
15878.267716 1
 
0.7%
22662.686719 1
 
0.7%
24285.13883 1
 
0.7%
24639.717868 1
 
0.7%
32717.874898 1
 
0.7%
ValueCountFrequency (%)
174558852128277.28 1
0.7%
12578616399561.484 1
0.7%
240000000.0 1
0.7%
236300800.0 1
0.7%
221838000.0 1
0.7%
164712000.0 1
0.7%
151701050.0 1
0.7%
132538829.666666 1
0.7%
126195160.0 1
0.7%
123643080.0 1
0.7%

YY
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.8
Minimum1998
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T12:46:28.692529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile2000.2
Q12007
median2010
Q32013
95-th percentile2016
Maximum2016
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5178658
Coefficient of variation (CV)0.0022479181
Kurtosis-0.078261169
Mean2009.8
Median Absolute Deviation (MAD)3
Skewness-0.71277456
Sum291421
Variance20.411111
MonotonicityNot monotonic
2023-12-11T12:46:28.835963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2014 12
8.3%
2012 12
8.3%
2011 12
8.3%
2010 12
8.3%
2009 12
8.3%
2008 12
8.3%
2013 12
8.3%
2015 12
8.3%
2016 11
 
7.6%
2007 9
 
6.2%
Other values (9) 29
20.0%
ValueCountFrequency (%)
1998 2
 
1.4%
1999 2
 
1.4%
2000 4
2.8%
2001 3
 
2.1%
2002 2
 
1.4%
2003 2
 
1.4%
2004 4
2.8%
2005 5
3.4%
2006 5
3.4%
2007 9
6.2%
ValueCountFrequency (%)
2016 11
7.6%
2015 12
8.3%
2014 12
8.3%
2013 12
8.3%
2012 12
8.3%
2011 12
8.3%
2010 12
8.3%
2009 12
8.3%
2008 12
8.3%
2007 9
6.2%

Interactions

2023-12-11T12:46:25.724961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:21.883015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:22.965295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:23.650173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:24.311802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:25.019631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:25.830701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:22.011468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:23.100915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:23.759483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:24.412558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:25.115281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:25.953133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:22.148144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:23.211331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:23.889151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:24.546582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:25.236173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:26.060937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:22.258404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:23.291980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:23.996421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:24.642795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:25.332110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:26.180331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:22.393187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:23.396055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:24.105305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:24.809539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:25.459757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:26.305100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:22.851159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:23.527808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:24.218268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:24.916715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:25.577540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:46:28.941187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YMIMPORT_TOT_AMTIMPORT_AVRG_AMTEXPNDTR_TOT_AMTEXPNDTR_AVRG_AMTYY
YM1.0000.0000.6150.0000.0001.000
IMPORT_TOT_AMT0.0001.0001.0000.0000.0000.000
IMPORT_AVRG_AMT0.6151.0001.0000.0000.0000.612
EXPNDTR_TOT_AMT0.0000.0000.0001.0000.6970.000
EXPNDTR_AVRG_AMT0.0000.0000.0000.6971.0000.000
YY1.0000.0000.6120.0000.0001.000
2023-12-11T12:46:29.088113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YMIMPORT_TOT_AMTIMPORT_AVRG_AMTEXPNDTR_TOT_AMTEXPNDTR_AVRG_AMTYY
YM1.0000.8020.0750.448-0.1550.997
IMPORT_TOT_AMT0.8021.0000.5220.6290.0340.796
IMPORT_AVRG_AMT0.0750.5221.0000.2810.2130.060
EXPNDTR_TOT_AMT0.4480.6290.2811.0000.6440.449
EXPNDTR_AVRG_AMT-0.1550.0340.2130.6441.000-0.161
YY0.9970.7960.0600.449-0.1611.000

Missing values

2023-12-11T12:46:26.476791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:46:26.582106image/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

YMIMPORT_TOT_AMTIMPORT_AVRG_AMTEXPNDTR_TOT_AMTEXPNDTR_AVRG_AMTYY
020140316907531457104004.8109243883768151631835.3571422014
12014027050759152611392.2777766060160922244504.0444442014
220140136252132218973300.0519824245651386001398.8564352014
3201312473271290512421818.648293395363524110376995.3832012013
42013111071431684421514692.45783125323242715084988.4959832013
520131024314231826078557.95525590836446397709.112013
620130928491410366898646.57627126108077486321568.3970932013
72013081005902586317283549.59278355283880669498948.5670092013
820130728581092584979284.42160220593358223587693.0696852013
920130638755773007046504.18181832499141675908934.849092013
YMIMPORT_TOT_AMTIMPORT_AVRG_AMTEXPNDTR_TOT_AMTEXPNDTR_AVRG_AMTYY
13520160217039432061934101.2553911996582722662.6867192016
1362016033452031335227418835.069102128206640101832.120732016
1372016041123038452854913782305.007323362311266294801.6810412016
13820160526098884082436870.59570410127836094564.2950512016
13920160630080862632978303.2306938969539688807.3227722016
14020160726874800422808234.1086722358021024639.7178682016
14120160816196981532125588.1272961209924015878.2677162016
1422016092576704421215328402.267697227778793135501.9589532016
1432016105833438412837465885.7597946913080044400.02016
1442016111503929600616241140.395248636351015687204.119872016